Apparatuses, methods and systems for advancement path benchmarking

ABSTRACT

The APPARATUSES, METHODS AND SYSTEMS FOR ADVANCEMENT PATH BENCHMARKING (“APB”) provides mechanisms allowing advancement seekers to identify, map out, structure and interact with various advancement paths to the seeker&#39;s goals. In one embodiment, the seekers are career advancement seekers, and the APB provides mechanisms allowing the seeker to explore various career paths and opportunities. In one embodiment, the APB interacts with a statistical engine, which allows seekers to map their experiences to various advancement states in the statistical engines state structure. By so doing, it allows seeker to explore multiple paths based on various criteria, and allows seekers to plan their career goals. In the process, the APB obtains and tracks information from a number of seekers allowing any one seeker to benchmark attributes of their advancement path against other seekers. In other embodiments, the seekers may be students wishing to advance their academic advancements. In yet other embodiments, the seekers are financial seekers who wish to achieve their financial goals.

RELATED APPLICATIONS

Applicant hereby claims priority under 35 USC §119 for U.S. provisionalpatent application Ser. No. 61/046,767 filed Apr. 21, 2008, entitled“APPARATUSES, METHODS AND SYSTEMS FOR CAREER PATHING,”.

The entire contents of the aforementioned application is hereinexpressly incorporated by reference.

FIELD

The present invention is directed generally to an apparatuses, methods,and systems of human resource management, and more particularly, toAPPARATUSES, METHODS AND SYSTEMS FOR ADVANCEMENT PATH BENCHMARKING.

BACKGROUND

People seeking employment traditionally have looked to job listings inprinted media such as newspapers or through employment and/or careerservices bureaus. More recently internet web services have come about,providing the ability to search through job postings and/or unstructuredjob bulletins. For example, job seekers can look to printed listings,university career websites and company websites in order to findinformation about the required and/or recommended qualifications neededfor specific jobs. To acquire a sense of which jobs a job seeker may besuited for and what advancement actions to take to acquire those jobs,job seekers may turn to career counselors or job-hunting books forrecommendations or advice.

SUMMARY

The APPARATUSES, METHODS AND SYSTEMS FOR ADVANCEMENT PATH BENCHMARKING(“APB”) provides mechanisms allowing advancement seekers to identify,map out, structure and interact with various advancement paths to theseeker's goals. In one embodiment, the seekers are career advancementseekers, and the APB provides mechanisms allowing the seeker to explorevarious career paths and opportunities. In one embodiment, the APBinteracts with a statistical engine, which allows seekers to map theirexperiences to various advancement states in the statistical enginesstate structure. By so doing, it allows seeker to explore multiple pathsbased on various criteria, and allows seekers to plan their careergoals. In the process, the APB obtains and tracks information from anumber of seekers allowing any one seeker to benchmark attributes oftheir advancement path against other seekers. In other embodiments, theseekers may be students wishing to advance their academic advancements.In yet other embodiments, the seekers are financial seekers who wish toachieve their financial goals.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying appendices and/or drawings illustrate variousnon-limiting, example, inventive aspects in accordance with the presentdisclosure:

FIG. 1 shows an overview of entities and data flow in one embodiment ofcareer statistical engine (“CSE”) operation;

FIG. 2 shows an implementation of application modules and databasescommunicatively coupled to the CSE in one embodiment of CSE operation;

FIG. 3A shows an implementation of combined logic and data flow foracquiring and processing career data inputs in one embodiment of CSEoperation;

FIG. 3B shows an implementation of combined logic and data flow forprocessing career data inputs in one embodiment of CSE operation;

FIG. 4A shows a schematic illustration of resume data record generationin one embodiment of CSE operation;

FIG. 4B shows a schematic illustration of experience to state conversionin one embodiment of CSE operation;

FIG. 4C shows an implementation of logic flow for experience to stateconversion in one embodiment of CSE operation;

FIG. 4D shows an implementation of a raw resume data record and a stateconverted resume data record in one embodiment of CSE operation;

FIG. 5A shows an implementation of a logic flow for determining stateselection;

FIG. 5B shows an implementation of combined logic and data flow forbuilding a state data record in one embodiment of CSE operation;

FIG. 6A shows an implementation of combined logic and data flow forprocessing state data to develop the statistical model in one embodimentof CSE operation;

FIG. 6B shows an implementation of combined logic and data flow forprocessing state data to develop the statistical model in anotherembodiment of CSE operation;

FIG. 7 shows an implementation of logic flow for development of apath-independent statistical model in one embodiment of CSE operation;

FIG. 8 shows an implementation of a path-independent state model datarecord in one embodiment of CSE operation;

FIG. 9 shows an implementation of logic flow for development of apath-independent statistical model with attributes in one embodiment ofCSE operation;

FIG. 10 shows an implementation of a path-independent model withattributes data record in one embodiment of CSE operation;

FIG. 11 shows an illustration of career path modeling usingpath-independent and path-dependent statistical models in one embodimentof CSE operation;

FIG. 12 shows an implementation of logic flow for development of apath-dependent statistical model in one embodiment of CSE operation;

FIG. 13 shows an implementation of a path-dependent statistical modeldata record in one embodiment of CSE operation;

FIGS. 14A-B show an implementation of logic flow for development and ofa path-dependent statistical model in another embodiment of CSEoperation; and

FIG. 15 is of a mixed block, data and logic flow diagram illustratingembodiments of the APPARATUSES, METHODS AND SYSTEMS FOR ADVANCEMENT PATHBENCHMARKING (hereinafter “APB”);

FIG. 16 is of a logic flow diagram illustrating embodiments of the APB;

FIG. 17 is of a logic flow diagram illustrating path-independent (i.e.,targeted) path construction embodiments of the APB;

FIG. 18 is of a logic flow diagram illustrating iteration-wisepath-independent path construction embodiments of the APB; and

FIG. 19 is of a logic flow diagram illustrating iteration-wisepath-dependent path construction embodiments of the APB; and

FIG. 20 is of a logic flow diagram illustrating N-part path-independentpath construction embodiments of the APB; and

FIG. 21 is of a logic flow diagram illustrating N-part path-dependentpath construction embodiments of the APB; and

FIGS. 22 and 23 are of a logic flow diagram illustrating gap analysisembodiments of the APB; and

FIGS. 24A-24H, 25A-25G, 26A-26H are of a screen shot diagramillustrating embodiments of the APB;

FIG. 27 is a block diagram illustrating job carousel embodiments of theAPB;

FIG. 28 is a logic flow diagram illustrating embodiments for invokingand displaying a APB;

FIG. 29 is a logic flow diagram illustrating embodiments for trackingseeker interactions with a APB;

FIG. 30 is a block diagram illustrating feedback interactions with aAPB; and

FIG. 31 is of a logic flow diagram illustrating benchmarking embodimentsfor the APB;

FIG. 32 is of a block diagram illustrating benchmarking interfaceembodiments for the APB;

FIG. 33 is of a mixed logic and block diagram illustrating path cloningembodiments for the APB;

FIG. 34 is of a mixed block and data flow diagram illustratingadvancement taxonomy embodiments for the APB;

FIG. 35 is of a block diagram illustrating advancement taxonomyrelationships and embodiments for the APB; and

FIG. 36 is of a block diagram illustrating embodiments of the APBcontroller;

The leading number of each reference number within the drawingsindicates the figure in which that reference number is introduced and/ordetailed. As such, a detailed discussion of reference number 101 wouldbe found and/or introduced in FIG. 1. Reference number 201 is introducedin FIG. 2, etc.

DETAILED DESCRIPTION

Career Statistical Engine

FIGS. 1-14B detail a career statistical engine (hereinafter, “CSE)component of the APPARATUSES, METHODS AND SYSTEMS FOR ADVANCEMENT PATHBENCHMARKING (hereinafter “APB”), which is detailed in the remainingfigures. The CSE allows for the generation and statistical mapping of anadvancement state structure, which furthers analysis associated with jobmarket analysis, job search strategies, career counseling, educationaladvancement, financial advancement, and/or the like. It is to beunderstood that depending on the particular needs and/or characteristicsof a job seeker, employer, career counselor, system operator, hardwareimplementation, network environment, and/or the like, variousembodiments of the APB may include a career statistical enginecomponent, which may include implementations allowing a great deal offlexibility and customization. The instant disclosure discusses anembodiment of the CSE within the context of job market analysis, careerpath modeling, job search strategies/recommendations, and/or the like.However, it is to be understood that the CSE may be readilyconfigured/customized for a wide range of other applications orimplementations. For example, aspects of the CSE may be adapted foroperation within a single computer system or over a network, for use ineducational path modeling and/or recommendations, task management, skilldevelopment; and/or the like. It is to be understood that the CSE may befurther adapted to other implementations or experience analysis andmanagement applications.

FIG. 1 shows an overview of entities and data flow in one embodiment ofCSE operation. The CSE 101 may be configured to allow a plurality of jobseekers (Job Seeker 1, Job Seeker 2, . . . , Job Seeker N) 105 tointeract with the CSE and/or engage CSE functionality. A job seeker maycommunicate with the CSE, such as via a communications network 110,and/or directly via a job seeker/network interface 115. The jobseeker/network interface 115 may be coupled to a CSE controller 120,which may serve a central role in facilitating CSE functionality andmediating communications and/or data exchanges between CSE modules,databases, and/or the like. The CSE controller 120 may be furthercoupled to a resume acquisition module 125, configured to receive andprocess resumes from job seekers 105. In alternative embodiments, theCSE may be configured to receive and/or process one or more of a varietyof different experiential sequences, such as educational transcripts,task lists, award histories, military records, and/or the like. The CSEController 120 may further be coupled to an analysis module 135,configured to analyze received resumes and to determine statisticalrelationships between and among experiences, job titles, educationlevels, accomplishments, and/or the like listed therein. The CSEController 120 may further be coupled to a plurality of databasesstoring data received and/or processed by the CSE. Such databases mayinclude, for example, an attributes database 138, storing attributesdata derived from submitted resumes; a state model database 140, storingelements of the state model; a resume database 130, storing receivedresumes and/or resume-derived information; and a user profiles database137, storing user accounts, user information, and/or the like. The CSEcontroller 120 may further be coupled to an application interface 145configured to process for and/or relay CSE processed data to one or moreexternal applications (Application 1, Application 2, . . . , Applicationm) 150.

FIG. 2 shows an implementation of application modules and databasescommunicatively coupled to the CSE 201 in one embodiment of CSEoperation. The illustrated CSE Application overview includes Career PathModeling 230, as well as Career Path User Interface system 240 featuresdriven data processed, analyzed and coordinated by the underlying CSE201 and/or associated Databases 205. In various embodiments, Career PathModeling 230 may be based on path-dependent 232 and/or path independent234 state model implementations and/or may further couple to arecommendation/recruiter engine 236. Similarly, in various embodiments,Career Path UI Modeling 240 may be based on path-dependent 242 and/orpath independent 244 state model implementations and/or may furthercouple to a recommendation/recruiter engine 246 The CSE 201 may alsocoordinate Career Data Structure Adapdation 250 and Career Benchmarking255 features. The CSE manages data associated with various systemprocesses in CSE Databases 205 that include State Model database 210,Taxonomy database 220 and Attribute Database 215 information, as well asthe underlying Video 222, People 224, Ads 226, and other content 228that may be incorporated into various implementations of the system.Further, in some implementations, the CSE Databases also coordinates therelationships/associations between these modules, as well.

FIG. 3A shows an implementation of combined logic and data flow foracquiring and processing career data inputs in one embodiment of CSEoperational. A plurality of individual career data inputs 301, such asresume data, profile data, and/or the like may be input to a career dataaggregation module 305. In one implementation, free-form resume data maybe parsed by an automated resume parses 310, such as may be based onresume templates. In another implementation, resume data may be input asstructured inputs in an online structured resume data entry module 315,such as a web form interface admitting experiential, educational, and/orthe like resume data inputs from users. In another implementation,future or prospective employment information may be entered via anonline future employment data entry module 320. In anotherimplementation, user profile information may be entered 321, such as maybe received from a user profile database. In one implementation, a seedset of raw seeker data (e.g., of structured resume data) may beprocessed initially by the CSE to yield an initial state model, topicmodel, and/or the like. For each job seeker 325, the CSE may read in rawseeker data 330, such as resume data, profile data, and/or the like.Elements of the raw seeker data, such as job titles, start and/or enddates of employment experiences, and/or the like may then be processedto discern a plurality of job state classifications, job states, states,and/or the like 335. In one implementation, statistical analysis of rawseeker job titles and/or other work experience data may be undertaken bya statistical analysis toolkit, such as by the Mallet toolkit availableat http://mallet.cs.umass.edu, to discern job states and/or otherclassifications. Elements of the raw seeker data, such as workexperience descriptions, may further be processed to discern a pluralityof topics and associated terms and/or phrases 340. For example, in oneimplementation, job seeker work experience description data may beprocessed by elements of the Mallet toolkit to discern a plurality oftopics comprising common terms and/o phases appearing in thosedescriptions. Discerned states and/or topics may then be coalesced intoa state model, and the state model may be stored in a database 345. Adetermination may then be made as to whether there is additional seekerprocessing to undertake 350. If so, then the CSE may return to 325.Otherwise, the CSE may proceed to determining and assigning topicweights to states in the state model, as shown in one implementation inFIG. 3B.

FIG. 3B shows an implementation of combined logic and data flow forprocessing career data inputs, in one embodiment of CSE operation, todetermine and assign topic weights to states in a state model. For eachstate of the plurality of states discerned by the statistical analysistoolkit in FIG. 3A, a weight may be assigned to each topic of theplurality of topics also discerned by the toolkit in FIG. 3A. Weightsmay, in one implementation, be based on the frequency with which termsassociated with topics appear in descriptions for resume workexperiences associated with states. For each state in the state model355, the CSE may determine work experiences, work experience datastructures, and/or the like associated to the state 360. In oneimplementation, such a determination may be made based on informationstored in or by the statistical analysis toolkit from FIG. 3A, theinformation being generated as part of the classification of workexperiences and the discernment of states at 335. The CSE may then parseterms from descriptions associated with the work descriptions 365 inorder to match those terms against terms associated with topics 370. Inthis manner, the CSE may determine which work experiences correspondingto a given state also correspond to a given topic or set of topics. TheCSE may then count the number of work experiences for a given state thatmatch a given topic 375 and divide by the total number of workexperiences associated with the state to determine the frequency, andaccordingly the weight, to assign to that given topic in associationwith that given state 380. The determined weights may then be associatedwith their corresponding topics within the state record for the givenstate 385. The CSE may then store the state model with states andtopics, including weights assigned to topics in association with eachstate, in a database 390. A determination may then be made as to whetheradditional processing of job seeker data is warranted 395. If so, theCSE may return to 355 and update topic weights. Otherwise, the CSE mayproceed to building a state data record, such as shown in oneimplementation in FIG. 5.

FIG. 4A shows a schematic illustration of resume data record generationin one embodiment of CSE operation, whereby a submitted resume may bemapped to states, topics, and/or the like using the state modelgenerated according to FIGS. 3A-3B. A submitted resume 401 may contain avariety of information describing experiences, attributes, and/or thelike associated with a job seeker. The resume 401 in the illustratedimplementation includes user contact information 403 (e.g., postaladdress, e-mail address, phone numbers, and/or the like), a workexperience sequence 406 comprising job titles 409 and description 412, alist of skills 415, a list and/or description of education experiences(e.g., schools attended, degrees received, grades, courses, and/or thelike) 418, a section listing and/or describing languages spoken 421,and/or the like. A state model 424 may serve to process resume 401 datainto one or more data records 431 configured for analysis and/orprocessing by CSE modules. In one implementation, the state model 424may process resume 401 information in conjunction with user profileinformation 428 and/or education information 429 to generate the one ormore data records 431. The state model 424 may, for example, analyze jobtitles 409 and/or descriptions 412 in order to map them to a pre-setlisting of job “states”. The work experience listing 406, thus, may beconverted into a state sequence 436 comprising a plurality of states 439associated with the job titles 409 and/or descriptions 412 from theresume 401.

Furthermore, an attributes model 427 may receive and/or process otherresume information, such as that external to the work experience listing406, to generate elements of a data record configured to analysis and/oruse by other CSE components. The attribute model 427 may further beconfigured to consider education 418 and/or relational taxonomy 430inputs, in addition to the other resume information, in generating thoseelements. In one implementation, the attribute model may map resumeinformation to elements of a pre-set listing of attributes. Thus, theskills 415, education 418, languages spoken 421, and/or the likeextracted from the resume 401 may be converted into an attributeslisting 442 comprising a plurality of attributes 445 corresponding tovarious elements of the resume information. Other resume information mayalso be included in a resume data record 431, such as may be collectedin an “Other” category 448 for subsequent reference and/or use. Theresume data record 431 may be associated with a unique resume identifier(ID) 433, based on which the record may be queried and/or otherwisetargeted.

FIG. 4B shows a schematic illustration of experience to state conversionin one embodiment of CSE operation, whereby an input resume may beconverted and/or otherwise mapped to states, topics, and/or the likeusing the state model generated according to FIGS. 3A-3B. Experienceslisted in a resume may be processed by one or more CSE state models toconvert those experiences to at least one of a list of pre-definedstates. In some cases, job seekers may use the same or similar jobtitles and/or descriptions to refer to jobs that may be very differentand/or that may correspond to different states within the CSE statemodel. FIG. 4B provides an illustration of CSE state resolution forsimilar resume work experience listings. Experience listings at 451 and460 each comprise the job title “Operations Manager”, but have differentjob descriptions. The CSE state model 454 may include a plurality ofstates, each having a plurality of corresponding job titles, and the CSEmay employ the model to determine which, if any, of the states havetitles matching the titles supplied at 451 and/or 460. In oneimplementation, different states may have common corresponding jobtitles. To resolve the appropriate state corresponding to each of thework experience listings 451 and 460, the CSE may analyze the listings'job description field for comparison with “topics” associated to eachstate. The job description in the listing at 451 includes keywords“shipping” and “receiving” that match topics in the state model 454entry corresponding to the state “Manufacturing Operations Manager” withstate number 23418, so the listing 451 is mapped to this unique state457. The job description in the listing at 460, on the other hand,includes the keywords “personnel” and “schedules”, matching topics inthe state model 454 entry for the state entitled “Staffing OperationsManager” with state number 52154, so the listing 460 is mapped to thisunique state 466. In one embodiment, a state structure may berepresented by way of database tables. In another embodiment, a statestructure, or limited subset thereof, may be represented as XMLinformation, which may be used for advancement pathing. In oneembodiment, the XML structure may take the following form:

<states>

<state id=“0” njobs=“3712” ntokens=“90708”>

-   -   <title>cna, certified nursing assistant, caregiver</title>    -   <jobtitles>        -   <jobtitle count=“260” pct=“7.0”>cna</jobtitle>        -   <jobtitle count=“142” pct=“3.8”>certified nursing            assistant</jobtitle>        -   <jobtitle count=“104” pct=“2.8”>[no title]</jobtitle>        -   <jobtitle count=“83” pct=“2.2”>caregiver</jobtitle>        -   <jobtitle count=“67” pct=“1.8”>home health aide</jobtitle>            . . .    -   <jobtitle count=“15” pct=“0.4”>residential counselor</jobtitle>    -   </jobtitles>    -   <topics>        -   <topic id=“494” n=“32601” words=“care residents home daily            living patients personal nursing aide activities”/>        -   <topic id=“696” n=“1719” words=“patients patient medical            insurance appointments charts doctors doctor procedures            care”/>            . . .    -   <topic id=“205” n=“544” words=“daily basis needed reports        activity log assist interacted complete schedule”/>    -   </topics>    -   <next>        -   <state id=“0” pct=“10.5” titles=“cna, certified nursing            assistant, caregiver” topics=“care patients treatment career            care unit medical activities children daily”/>    -   <state id=“268” pct=“4.6” titles=“cna, certified nursing        assistant, caregiver” topics=“care cleaning job job assist        helped shift duties clean food”/>        . . .    -   <state id=“45” pct=“1.1” titles=“medical records clerk, medical        transcriptionist, file clerk” topics=“medical records answered        phones office answer office patients data data”/>    -   </next>    -   <prev>        -   <state id=“999” pct=“23.0” titles=“[First job]”            topics=“[First job]”/>    -   <state id=“0” pct=“10.2” titles=“cna, certified nursing        assistant, caregiver” topics=“cna, certified nursing assistant,        caregiver”/>        . . .        <state id=“243” pct=“0.9” titles=“administrator, executive        director, director of nursing” topics=“administrator, executive        director, director of nursing”/>    -   </prev>

</state>

<state id=“1” njobs=“3570” ntokens=“113569”>

. . .

</state>

<states>

The XML form including a title, other analogue job titles and relatedfrequency counts and likelihood percentages, topics, next states andprevious states with frequency occurrences, and/or the like.

Job listings with different job titles may also be mapped to the samestate by a CSE state model 454. The listing at 469 includes a job titleof “Facilities Manager”, which matches one of the titles for the state“Manufacturing Operations Manager” (though possibly other states aswell) in the CSE state model 454. The listing 469 further includes a jobdescription comprising the keywords “shipping” and “receiving”, whichmatch topics associated with the state “Manufacturing OperationsManager”, so the listing 469 is mapped to the unique state 475, which isthe same as the state at 457 despite the different job title in theoriginal listing.

FIG. 4C shows an implementation of logic flow for experience to stateconversion in one embodiment of CSE operation. The logic in FIG. 4C maybe applied, for example, to a work experience listing extracted from aresume or curriculum vitae (CV). In alternative implementations, thelogic in FIG. 4C could be applied to job listings from other sources,such as career development resources, school and/or corporate websites,and/or the like. A job title may be queried and/or extracted from thelisting 476 and compared with a plurality of job titles corresponding tostates in the state model 477 in order to determine whether there existany states having matching job titles 478. If there are no matches, thenthe CSE may engage an error handling procedure, try approximate matchingof the job titles, and/or the like 479. For example, in oneimplementation, the CSE may perform a search based on a subset of thecomplete job listing job title to find approximate matches. In anotherimplementation, the CSE may seek states having job titles with subsetsmatching the job title extracted from the job listing (e.g., a statemodel job title of “Manufacturing Operations Manager” may be deemed amatch for an input job title of “Manufacturing Manager”). In stillanother implementation, an error message may be returned for the inputjob title and/or the job title may be set to a null state.

If one or more matches are established at 478, a determination may bemade as to whether there are multiple matching states 480. If there isonly one matching state, then the CSE may immediately map the inputlisting to the matching state 481. Otherwise, the CSE may query and/orextract a job description from the input listing 482 and parse key termsfrom that description 483. Parsing of key terms may be accomplished by avariety of different methods in different implementations and/orembodiments of CSE operation. For example, in one implementation, theCSE may parse all terms from the description having more than a minimumthreshold number of characters. In another implementation, the CSE mayfilter all words in the description that match elements of a listing ofcommon words/phrases and extract the remaining words from thedescription. The parsed key terms may then be compared at 484 to statemodel topics corresponding to the matching states determined at 477-478.A determination is made as to whether there exist any matches betweenthe job description terms and the state topics 485 and, if not, then oneor more error handling procedures may be undertaken to distinguishbetween the matching states 486. For example, in one implementation, theCSE may choose a state randomly from the matching job states and map theinput listing thereto. In another implementation, the CSE may present ajob seeker, system administrator, and/or the like with a messageproviding a selectable option of the various matching states, to allowfor the selection of a desired match.

If a match exists at 485 between description key terms and state topicsin the CSE state model, then a determination may be made as to whetherthere exists more than one matching state 487. In one implementation,this determination may only find that multiple matches exist if thenumber of key terms matching state topics is the same for more than onestate (i.e., if one state has more matching topics than another, thenthe former may be deemed the unique matching state). If there are notmultiple matching states, then the input listing may be matched to theunique matching state 489. If, on the other hand, multiple matches stillexist, then the CSE may, in various implementations, undertake any of avariety of different methods of further discerning a unique matchingstate for the input listing. For example, in one implementation, the CSEmay choose randomly between the remaining states. In anotherimplementation, the CSE may provide a list of remaining states in amessage to a job seeker, system administrator, and/or the like to permitselection of a desired, unique state. In another implementation, the CSEmay map the job listing to all of the multiple matching states.

In one implementation, logic flow similar to that described in FIG. 4Cmay be employed to map other resume information, such as educationexperiences, skills, languages spoken, honors and/or awards, travel,and/or the like to one or more attribute states stored in and/or managedby the CSE, a CSE state model, a CSE attribute model, and/or the like.

FIG. 4D shows an implementation of a raw resume data record and a stateconverted resume data record in one embodiment of CSE operation. The rawresume data record 490 is indexed by a resume ID 491, and includes avariety of resume data, including contact information 492, a jobsequence listing 493, and other information 494 such as education,skills, honors/awards, and/or the like. The corresponding stateconverted resume data record is shown at 495, and includes a statesequence 496 corresponding to the job sequence 493, as well as a seriesof attributes 497 that are based on the other resume information. Thestate converted resume data record also may incorporate other resumedata 498.

FIG. 5A shows an example implementation of a logic flow for determiningstate selection. In 7 one embodiment, the APS may receive a workexperience query 501. The work experience query 3 may be processed bythe APS to determine potential matching states 502. The retrieved statesmay then be ranked at 503 (note: ranking schema are also discussed inparagraphs 0088, 0099, and 0143). The APS may determine whether or notan auto-choose mechanism is to be engaged 504. If such a 1 mechanism isset up, the APS may choose the highest ranked state automatically 505and associated z with the work experience query item. If the APS doesnot choose the APS will present the results, 3 for example, in rankedorder 506 (note: ranking schema are also discussed in paragraph 0088,0099 1 and 0143). Once the user makes a selection, the APS may receivethe state selection and the state s selection and associate workexperience query item 507.

FIG. 5B shows an implementation of combined logic and data flow forbuilding a state data record in one embodiment of CSE operation. Foreach job seeker data record 508, such as may correspond to resume and/orprofile data submitted by the job seeker, the CSE may process the seekerrecord to create and/or update one or more state models and/or datatables 509. An example of such data processing in one implementation isshown at 510, wherein a unique state ID is created and state data ismapped thereto. Associated with the state ID may be one or more jobtitles, topics and/or topic IDs, skills, education information, salaryinformation, experience information, length and/or time at a job, and/orthe like. The state record may further include links to next state IDs,previous state IDs, and/or external database links, such as toassociated videos, people and/or profiles, ads, and/or other content.The state record may be stored in and/or used to update the state modelfor storage in a state model database 515. A determination may then bemade as to whether additional state processing is to be undertaken 520.If so, then the CSE may return to 501 and draw on the next seeker datarecord. Otherwise, the CSE may move to processing state data to developthe statistical database and/or perform incremental state discovery,such as by the embodiments shown in FIGS. 6A-6B.

FIG. 6A shows an implementation of combined logic and data flow forprocessing state data to develop the statistical model in one embodimentof CSE operation. For each state data record 601, the CSE may update acareer path and/or state model topology and/or topology weights based onanalysis of the data record 605. In one implementation, a career pathand/or state model topology may comprise a plurality of relationshipsbetween job states established based on the frequency of occurrence ofsuch relationships in the work experience sections of analyzed resumes.The CSE may also be configured to add new nodes to the career pathand/or state model topologies as necessary 610, such as if a newlyanalyzed resume introduces a relationship between job states that hadnot been seen in previously analyzed resumes. The updated career pathand/or state model topology may be stored in a database 615 and adetermination made as to whether additional statistical analysis isrequired 620. If so, then the CSE returns to 601 and proceeds withadditional statistical analysis of the state data record and/or moves toa next state data record. Otherwise, the career path and/or state modeltopology may be provided for access and/or use by other career pathfeatures and/or functions 625.

FIG. 6B shows an implementation of combined logic and data flow forprocessing state data to develop the statistical model in anotherembodiment of CSE operation.

For each state data record 630, the CSE may analyze the record using anyof a variety of statistical analysis tools. Numerous methods of topicmodeling may be employed as discussed in: “Latent Dirichlet Allocation,”D. Blei, A. Ng, M. Jordan, “The Journal of Machine Learning Research”,2003. Markov models may also be employed as discussed in: “A tutorial onhidden Markov models and selected applications in speech recognition,”L. Rabiner, Proceedings of the IEEE, 1989. In one embodiment, MalletProcessing tools 635 may also be employed, such as may be found athttp://mallet.cs.umass.edu. The analysis may include aggregation and/oranalysis of user individual state records 640, aggregation and/oranalysis of user state chain records 645, and/or aggregation and/oranalysis of user historical parameter(s) 650. User historical parameters655 may, for example, comprise salary, location, state experienceduration, subjective experiences associated with job states, benefits,how the job was obtained, other benchmarking and/or user generatedcontent, and/or the like. The statistics associated with the staterecord may be summed 660 and added to the state statistical records inone or more state models stored in a state model database 665. Adetermination may be made as to whether additional statistical analysisof state data records is to be undertaken 670. If so, then the CSE mayreturn to 630 to proceed with additional analysis of the state datarecord and/or to move to the next state data record. Otherwise, thestate model may be provided for path modeling 675, benchmarking 680,and/or the like applications.

FIG. 7 shows an implementation of logic flow for development of apath-independent statistical model in one embodiment of CSE operation.In one embodiment, a path-independent statistical model may comprise acollection of job states, each having corresponding probabilities formost likely next and previous job states, wherein the probabilitiesdepend only on the job state itself and not on any prior history of jobstates. A resume, profile data, and/or the like may be received at 701,such as from a resume database, and a job and/or other work experiencesequence extracted therefrom 705. Jobs from that sequence may be mappedto corresponding states in a CSE state model 710, such as according tothe embodiments described in FIGS. 4A-D. Then, for each job state in thesequence 715, the CSE may query a next job state (J_(n)) and a previousjob state (J_(p)) in the sequence 720, where a null state may be set toJ_(p) for the first state in the sequence and to J_(n) for the laststate in the sequence. A state record corresponding to the current stateunder consideration (715) may be retrieved 725, such as from a CSE statemodel, and a determination may be made as to whether J_(n) exists in thestate record 730. For example, the CSE may seek J_(n) in a listing ofcommon next job states corresponding to the given job state. If J_(n)does exist in the record, then a number of occurrences, N(J_(n)), ofJ_(n) as a next state for the state under consideration may beincremented 735. Otherwise, J_(n) may be appended to the listing of nextstates for the state under consideration 740 and a value for the numberof occurrences of J_(n) initialized 745.

The CSE may also determine whether J_(p) exists in the state record,such as in a listing of common previous job states corresponding to thestate under consideration 750. If so, then a number of occurrences,N(J_(p)), of J_(p) as a previous state for the state under considerationmay be incremented 755. Otherwise, J_(p) may be appended to the listingof previous states for the state under consideration 760 and a value forthe number of occurrences of J_(p) initialized 765. The CSE may thenincrement a total number, N_(tot), associated with the number of resumesused to update the particular state entry of the path-independentstatistical model 770. The CSE may then determine probabilitiescorresponding to J_(p) and J_(n) by dividing N(J_(p)) and N(J_(n)) eachrespectively by N_(tot) 775. These probabilities may, for example,provide an indication to job seekers of the likelihood of changing to orfrom a job from another job, based on the accumulated resume records ofother job seekers who have held those jobs. The state record with theupdated probability values may be persisted at 780, such as by beingstored in a database.

FIG. 8 shows an implementation of a path-independent state model datarecord in one embodiment of CSE operation. The data record in FIG. 8may, for example be generated and/or updated by the logic flow shown inFIG. 7. The data record may, in one implementation, correspond to aunique job state in the CSE state model, indexed by a state ID 801. Alisting of next states 805 may include a plurality of states andcorresponding probabilities 810, such as may be determined according tothe logic in FIG. 7. Similarly, the data record may include a listing ofprevious states 815 comprising states and corresponding probabilities820. Additional data associated with the state may be stored in thestate record 825, such as but not limited to a total number of resumesanalyzed for the state in question, a confidence metric describingconfidence in and/or reliability of the probabilities in 810 and/or 820,state job titles, state topics, and/or the like.

FIG. 9 shows an implementation of logic flow for development of apath-independent statistical model with attributes in one embodiment ofCSE operation. In one embodiment, a path-independent statistical modelwith attributes may comprise a collection of job states, each havingcorresponding probabilities for most likely next and previous jobstates, wherein the probabilities depend on the job state itself and onthe identity of one or more associated attributes, but not on any priorhistory of job states. Resume and/or profile data may be received at901, and a job experience sequence may be extracted therefrom 905. Jobsfrom the job sequence may then be mapped to CSE state model job statesat 910, such as according to the embodiments described in FIGS. 4A-D.The CSE may also extract additional resume data 915, such as but notlimited to education levels, schools attended, awards and/or honors,skills, languages spoken, number of years of experience, salary levels,certifications and/or licenses possessed, and/or the like. Theadditional resume data may be mapped to attribute states in the CSEstate and/or attribute model 920, such as in a manner similar to mappingof job sequence listings to job states. Then, for each state in thesequence of job states 925, the CSE may query the next job state (J_(n))and previous job state (J_(p)) in the sequence 930, with a null stateassigned to J_(n) for the last state of the sequence and to J_(p) forthe first state of the sequence. The CSE may also retrieve the staterecord for the current state 935.

Then, for each attribute in the resume 940, the CSE may determinewhether J_(n) exists in the state record in correspondence with thatattribute 945, such as in a listing of common next job statescorresponding to the state and attribute under consideration. If so,then a number of occurrences, N(J_(n)), of J_(n) as a next state for thestate and attribute under consideration may be incremented 950.Otherwise, J_(n) may be appended to the state record in association withthe particular attribute 955 and a value for the number of occurrencesof J_(n) initialized 960. A determination may then be made as to whetherJ_(p) exists in the state record in correspondence with the attributeunder consideration 965. If so, then the number of occurrences,N(J_(p)), of J_(p) as a previous state for the state and attribute underconsideration may be incremented 970. Otherwise, J_(p) may be appendedto the state record in association with the particular attribute 975 anda value for the number of occurrences of J_(p) initialized 980. A totalnumber of instances may then be incremented 985, and probabilities forJ_(n) and J_(p), corresponding to the proportion of resumes having theattribute under consideration and those job states respectively beforeand after the job state under consideration, may be determined as theratio of each of N(J_(n)) and N(J_(p)) with N_(tot) 990. The staterecord, with updated probability values, may then be persisted at 995,such as by storing the record as part of a CSE state model in adatabase.

FIG. 10 shows an implementation of a path-independent model withattributes data record in one embodiment of CSE operation. The datarecord in FIG. 10 may, in one implementation, be generated by a methodsimilar to that shown in FIG. 9. The record, corresponding to aparticular job state, may be identified by a unique state ID 1001, andmay further include a plurality of attributes (1005, 1030). Eachattribute, in turn, may include listings of next states 1010 and ofprevious states 1020, states comprising states and associatedprobabilities (1015, 1025), such as may be determined by the methoddescribed in FIG. 9. In one embodiment, a hierarchy of states may begenerated by traversal of interconnected state structures.

FIG. 11 shows an illustration of career path modeling usingpath-independent and path-dependent statistical models in one embodimentof CSE operation. The CSE may, in some embodiments, operate to take oneor more job inputs and return a job output, wherein the job outputcomprises a prediction of a most likely next job and/or otherwisestatistically noteworthy result based on the inputs. In FIG. 11, a usermay provide an experience sequence comprising five jobs (J1 1105, J2,1110, J3 1115, J4 1120, J5 1101) as inputs to the CSE. In oneembodiment, the CSE comprises a path-independent model 1125 that maygenerate an output job state J6 1130 based only on a single job state(e.g., J5 1101). In an alternative embodiment, the CSE comprises apath-dependent model 1135 that takes multiple jobs as inputs (J1 1105,J2, 1110, J3 1115, J4 1120, J5 1101) to generate and return an outputjob state J6′ 1140. The embodiments described in FIGS. 7-10 are directedto generation of the path-independent CSE state model.

FIG. 12 shows an implementation of logic flow for development of apath-dependent statistical model in one embodiment of CSE operation. Inone embodiment, a path-dependent statistical model may comprise acollection of job states, each having corresponding probabilities formost likely next and previous job states, wherein the probabilitiesdepend on the history of jobs leading up to the most recent job state.Though FIG. 12 is directed to an implementation bereft of attributeconsideration, it should be recognized that aspects of FIGS. 9-10 couldbe incorporated to yield an attribute-sensitive, path-dependent statemodel. The CSE may receive resume data, profile data, and/or the like,such as from a resume database, at 1201, and extract a job sequencecomprising a plurality of jobs (J1, J2, . . . , JN) therefrom 1205 whichmay subsequently be mapped to a sequence of job states. Then, for eachstate (J_(i)) in the sequence 1220, the CSE may retrieve a state recordcorresponding to state J_(i) 1225 and set an indexing variable, m, equalto i+1 1230. A determination may then be made as to whether a fieldcorresponding to the state J_(m) exists in the J_(i) state record 1235.If so, then a number of occurrences, N_(i . . . m) of that sequence ofjob states (J_(i) to J_(m)), is incremented 1240. Otherwise, the J_(m)field is appended to the state record 1245, and the value of a number ofoccurrences corresponding to the job sequence is initialized 1250. Atotal number, Ntot_(i . . . m), of instances (e.g., the number ofresumes analyzed) may then be incremented 1255, and a probability forthe sequence determined by dividing the number of occurrences of the jobsequence by the total number of instances 1260 may be calculated. Adetermination may then be made as to whether there are more states toanalyze in the job sequence 1265. If so, the indexing variable m isincremented 1270, and the CSE returns to 1235. Otherwise, when allstates in the sequence are exhausted, the J_(i) state record ispersisted, and the CSE moves to the next job state at 1220 (e.g., byincrementing i) 1275.

To further illustrate the embodiment described in FIG. 12, the followingexample may be considered. A resume may include a work experiencehistory comprising a sequence of three jobs: J1, J2 and J3. The logic inFIG. 12 would first update a CSE state model based on the job sequenceJ1 to J2, specifically updating the probability associated with J2 in aJ1 state record. Next, the CSE state model would update a probabilityassociated with J2 to J3 in the J1 state record. Then, the CSE statemodel would retrieve the J2 state model and update a probabilityassociated with J3 therein. In this manner, the CSE state model willcontain information pertaining to probabilities of all the sequences andsub-sequences of the work experience listings in the resumes that itanalyzes (in this exemplary case, those sequences and sub-sequences are:J1, J2; J1, J2, J3; and J2, J3).

FIG. 13 shows an implementation of a path-dependent statistical modeldata record in one embodiment of CSE operation. The state data recordshown in FIG. 13 may, in one implementation, be generated by a logicflow similar to that shown in FIG. 12. The state, here labeled A, towhich the data record corresponds may be identified by a state recordidentifier 1301. The state record may further include a second tier of“next job” states, each characterized by at least a state identifier anda probability 1305. For example, in FIG. 13, a next state is labeled Band has a probability labeled AB corresponding to the proportion ofresumes analyzed wherein an individual having job A moved to job B.Under each of the second tier states, there may further exist third tierstates 1310, fourth tier states 1315, etc., each including at least astate identifier and a probability associated with the sequence leadingto the current state from each state in the higher tiers. For example,in FIG. 13, the state labeled D at the fourth tier shown at 1315 isassociated with a probability labeled ABCD that characterizes theproportion of resumes wherein an individual had the sequence of jobs A,B, C and D.

FIGS. 14A-B show an implementation of logic flow for development and ofa path-dependent statistical model in another embodiment of CSEoperation. Models similar to that shown in FIGS. 14A-B may, in someimplementations, include a two-stage method, the first comprising asetup stage wherein the model is established as a collection of jobstate couplets (FIG. 14A), and the second comprising an application ofthe model to a specific job sequence and/or target job state to yield atarget job state probability (FIG. 14B). Resume data, profile data,and/or the like is received at 1401 and a job sequence (J1, J2, . . . ,JN) is extracted 1405. The job sequence may then be converted intocorresponding job states (JS) 1410, such as according to the embodimentsdescribed in FIGS. 4A-D. Then, for each JS in the sequence 1420, the CSEmay read the JS and the next state (NS) in the sequence to establish acouplet comprising a pointer between JS and NS 1415. The state model maythen be queried 1420 to determine whether a match exists to the JS/NScouplet 1425. If not, then an entry may be created in the state modelcorresponding to the couplet 1430 or, if so, then the number ofinstances for that couplet's may be incremented 1435. The couplet entrymay be stored 1440 in association with a user ID, resume ID, and/orother identifier associated with the resume from 1401, as well as with aJS sequence number (n), associated with the position of JS in the jobsequence from 1405. A determination may then be made as to whetheradditional states exist in the sequence 1445 and, if so, the CSE mayreturn to 1415 to analyze the next sequence state.

FIG. 14B illustrates an implementation of logic flow for application ofthe state model to obtain a probability associated with a given targetjob state given a sequence of past job states, in one embodiment of CSEoperation. The target job state is obtained at 1450, and the sequence ofpast job states, comprising a plurality of couplets of job state andnext state, is entered 1455. Then, for each couplet or pair, the statemodel may be searched 1457 to obtain matching pairs 1459. The CSE maythen apply a filter to extract desired and/or relevant matches. Forexample, the CSE may query the CSE 1461 to obtain results 1463 out ofthe matching pairs from 1459 that have common associated UserIDs acrosspairs. For example, the CSE may have found pairs (A, B) and (B, C) at1457-1459, corresponding to jobs A, B and C. To establish that thesequence A to B to C exists for any specific users, the CSE could thenseek common user IDs existing in both the (A, B) and (B, C) records.

The CSE may also want to ensure that the sequence exists in the properorder. For example, if a common user ID exists in the (A, B) and (B, C)records, this does not necessarily imply that a user has the specificjob sequence A to B to C in their resume and/or profile data. The usermay, instead, have a sequence such as B to C to A to B. The CSE may,therefore, query results for proper JS chain sequence ordering 1465,such as may be based on the JS sequence number (n) stored at 1440.

The CSE may thus obtain 1467 and count 1469 the non-targeted results,that is the single-resume job sequence matches to the JS existing chainfrom 1455, but not including the target state from 1450. The CSE maythen search the state model 1471 to obtain “goal results” 1473comprising couplets of the last state in the JS existing chain with thetarget state. A filter process similar to that shown at 1461-1465 maythen be applied to the sequence comprising the non-targeted results plusthe goal results 1475. The number of filtered goal results are counted1477 and the ratio of the number of goal results to the number oftargeted results may be computed 1479, stored, and/or the like. Thisratio may be interpreted as the proportion of analyzed resumes havingthe sequence of jobs corresponding to the JS existing chain from 1455leading into the target job state from 1450.

APB

FIG. 15 is of a mixed block, data and logic flow diagram illustratingembodiments of APPARATUSES, METHODS AND SYSTEMS FOR ADVANCEMENT PATHBENCHMARKING (hereinafter “APB”). From a high level, the APB 1501 allowsusers (e.g., advancement “seekers”) 1533 a to interact with APB servers1502 through interfaces on their client(s) 1533 b across acommunications network 1513. Although the following discussion willfrequently use examples of seekers wishing to advance their careers, itshould be noted, that such seekers may similarly use the APB to advancetheir educational achievement, their financial goals, and/or the like.To that end, seekers 1533 a may provide 1533 b relevant (e.g., job)experiences they have had leading up to their current desire to seekadvancement beyond their past and current experiences 1505, 1506, 1507(hereinafter “experience information”) to the APB. Similarly, seekers1533 a may provide 1533 b targeted advancement milestones, objectivesand/or goals (hereinafter “advancement information” or “targetinformation”) to the APB. In turn, the APB 1501 may obtain thatadvancement experience information 1510 and use that information 1502 toprovide the seeker with next states in their advancement goals 1509.

Upon obtaining the user advancement experience information 1510, the APBmay analyze the experience information (e.g., and perhaps otherinformation associated with the user found in the user's profile)against a state structure 1512. By analyzing the advancement seeker'sexperiences and goals against a statistical state structure, the APB maydetermine what next states 1514 may form the advancement seeker's nextadvancement milestone(s) and/or paths to their desired milestones and/oradvancement goals 1509. It should be noted that in one embodiment, thestate structure may take the form of generated by the CSE. In oneembodiment, the state structure is stored in APB state structuredatabase table(s); as such, the state structure may be queried withadvancement experience information, advancement information, experienceinformation, state identifier (e.g., state_ID), proximate stateidentifier (e.g., next_state_ID), topics/terms, topic_ID, and/or thelike. When queried, the state structure may return state records (i.e.,states) that best match the query select commands, and those states maythemselves further refer to other proximate states; where the proximatesates are related advancement states (hereinafter “adjacent state,”“advancement state,” “next state,” “proximate state,” “related state,”and/or the like) that may include likelihoods of moving from the stateto the related advancement state. Upon determining what next states mayform the advancement path and/or milestone for the seeker 1514, the APBmay generate a user path topology showing the user their advancementpath. This topology may be used to update the seeker's client 1533 bdisplay 1518 with an interactive (e.g., career) advancement path.

FIG. 16 is of a logic flow diagram illustrating embodiments of the APB.A user need not be, but may be, logged in to an existing account to theAPB to make use of its advancement pathing abilities 1601. In eithercase the user will engage the APB (for example, in a web embodiment ofthe APB); a user may engage the APB by navigating their web browser toan address referencing the APB's information server, which will act asan interface/gateway between the seeker and APB. It should be noted thata web interface is one of many interface and/or mechanisms by which theAPB may be deployed and/or implemented; for example, in alternativeembodiments the APB may be a stand alone application, a server messagingsystem that accepts inputs and provides outputs to disparate clients,etc. In accessing the APB 1601, the seeker may start to provideexperience advancement information. The experience advancementinformation may include both desired advancement milestones and/or goals(although this is not required) and experience information, whichincludes experience the seeker already has. The experience informationmay be provided by way of submission of structured information via a webform, parsing submitted resume's (e.g., via attachment and/or uploadingof a resume file), aggregating experience information in a profile overtime, allowing the user to select a pre-existing state matching theirown (e.g., letting them find a job/title/occupation matching theircurrent occupation) in graph topology representing an hierarchicalinterconnected state structure (e.g., see 2405 of FIG. 24), and/or thelike. In one example embodiment, the user may submit current workexperience via web form, which may include: the dates of employment, theemployer's name (e.g., employing company), seeker title/position,descriptive resume information about their employment, and/or the like1602. In another embodiment, the seeker may submit experienceinformation beyond their instant post that includes: previous positions,their educational background, and/or the like 1602. In addition, theseeker may similarly provide their advancement information. For example,the seeker may provide that they currently have the title of RetailAdministrator, without more, and see what are the next most likelycareer path opportunities from that role, without having any explicitadvancement goal. However, should the seeker also provide a milestoneand/or goal, e.g., Manager of a retail chain, then the APB willconstruct paths and experiential states that show the seeker's thedifferent routes by which the seeker may advance to their desiredmilestone/goal. It should be noted that build/find path facilities thatare described are not exclusive mechanisms for building paths, andbrowsing through the topology is also supported as will be detailed infurther figures.

Depending on the information supplied by the seeker and the seeker'sdesire to see advancement path variations, the APB may provide at leastthree different types of advancement path analysis 1604: targeted paths1623 (see FIG. 17 for examples), iteration-wise paths 1624 (see FIG. 18for examples), and N-part open-ended paths 1625 (see FIG. 19 forexamples). Upon obtaining selections from a seeker for one of the typesof analysis 1604, or upon making a determination that the seekerprovided advancement experience information best suitable for only oneof the types of analysis 1604, and upon performing the respectiveanalysis 1623, 1624, 1625, the APB will construct an advancement pathbased on the seeker's advancement experience information and present itbased on a selected visualization style 1606. The visual style may beselected by the seeker from a set of visualization template styles, orselected by the APB and/or administrator.

Upon applying the visualization style to the determined advancement path1606, this visualization of the advancement path is provided to theclient for display 1608. It should be noted that, e.g., career,advancement paths may be stored and shared as between users. In oneembodiment, regardless of how the path is determined, The seeker maythen interact with the visualized path and the APB may obtain the userinteractions 1609. The APB may then determine if any of the userinteractions provided new experience information, advancementinformation, or modifications to the constructed path such that newpaths need to be generated 1610. If the interactions are such thatrequire providing more information 1610 then the seeker is allowed toagain provide more advancement experience information or otherwisemodify factors affecting the generated path 1602. Otherwise 1610, theAPB will determine if the user interactions 1609 require that thedisplay is updated 1611. If the user modified or provided inputs,indicia and/or otherwise operated on path objects or values that requirethat the path visualization and/or screen is updated, the data obtainedfrom the user interactions 1609 is then used by the APB to effectupdates the career path display 1608. Otherwise, the APB may conclude1612 and/or wait for further interactions.

Path-Independent Targeted APB

FIG. 17 is of a logic flow diagram illustrating path-independent (i.e.,targeted) path construction embodiments for the APB. It should be notedthat FIGS. 18, 19, 20 and 21 offer mechanisms that may supplement,alter, and/or otherwise provide embodiments alternative to FIG. 17. Uponobtaining seeker experience advancement information 1602 and determiningthat a targeted independent advancement path is desired 1604 of FIG. 16,the APB will use advancement experience information to establish a startstate and a target state 1714.

In one embodiment, the seeker experience advancement information may beprovided by the seeker by way of a web form as shown in FIGS. 24, 25,26. The web form may be served by an information server, and the webform fields may serve as a vessel into which the seeker may providestructure information, attach a resume, specify advancement experienceinformation, or otherwise provide both experience information andspecify the desired advancement milestone and/or goal. In oneembodiment, this information is submitted to the APB and is stored asfield entries in the APB database table for the seeker, e.g., in aseeker profile record. In another embodiment, this information isprovided in XML message format such as the following:

<Advancement Experience Information ID = “experience12345”>  <ExperienceInformation>   <Job 1>    <Title 1> Assistant to the ManagementConsultant </Title 1>    <Start_Date> 03/14/89 </Start_Date>   <End_Date> 5/15/03 </End_Date>    <DescriptionTerms>     <Term1>training </Term1>     <Term2> process </Term2>     <Term3> development</Term3>     <Term4> costs </Term4>     <Term5> coffee </Term5>   </DescriptionTerms>   </Job 1>   <Job 2>    <Title 1> AssistantManagement Consultant </Title 1>    <Start_Date> 05/16/03 </Start_Date>   <End_Date> 6/15/09 </End_Date>    <DescriptionTerms>     <Term1>training </Term1>     <Term2> process </Term2>     <Term3> development</Term3>     <Term4> costs </Term4>    </DescriptionTerms>   </Job 2>  <Job 3> . . . </Job 3>  </Experience Information>  <AdvancementInformation>    <DescriptionTerms>     <Term1> Executive </Term1>    <Term2> Consultant </Term2>    </DescriptionTerms>   </AdvancementInformation>   <Filter Information>    <DescriptionTerms>     <Filter1>Salary > $100,000 </Filter1>     <Filter2> Region Zipcode [e.g., 10112]< 25 miles </Filter2>     <Filter3> Degree < Masters </Filter3>    <Filter4> Growth > 20% </Filter4>     <Filter5> Relocation Expenses= TRUE </Filter5>     <Filter6> Expected Next Year Occupation DemandLevel > 20,000 jobs </Filter6>     <Filter7> Signing Bonus > $10,000</Filter7>     <Filter8> Annual Technology Stipend > $5,000 </Filter8>    <Filter9> Annual Health Insurance Stipend > $25,000 </Filter9>    <Filter10> Regular Travel = False </Filter10>     <Filter11> SalaryLevel > [Top] 10% [in field] </Filter11>    </DescriptionTerms> </Advancement Experience Information ID >

Turning for a moment to FIGS. 24 and 25, the Figures show alternativeexample embodiments of how start states and target states may beselected. In another embodiment, the seeker may navigate a statestructure topology such as may be seen in 2405 of FIG. 24. This may beachieved by clicking on advancement topics 2409 that will zoom in toshow various advancement states 2412, which the user may specify asbeing start state, intermediate state, and end state 2414 of FIG. 10. Inyet another embodiment, the seeker may enter a topic, career choice,title or other information indicative of a desired state 2424 in afield, which will be submitted as a query to the state structure; thestate structure in return will return states that most closely match thesupplied search term 2426, which the user in turn may select 2426 andwhich may be displayed, zoomed in on, and further manipulated in atopology display area 2427 of FIG. 24. In yet another embodiment, theuser may similarly supply terms to identify both a start state andtarget state 2524, 2526, 2528, which will form the basis of a pathbetween start state and target state 2533 of FIG. 25; in thisembodiment, the APB similarly identify potential matching states foreach of the supplied terms 2528 and constructed various paths that matchthe results form those terms 2537 of FIG. 25. The seeker may then selectform the list of paths 2537 and the path topology display area 2539 willbe updated to reflect the selected path 2539 of FIG. 11. So for example,the seeker may specify their current position an Assistant Administratorin a retail hardware store and that they have a goal of becoming aRegional Manager of a chain of hardware stores. In such an embodiment,the APB may use the provided seeker advancement information as a basiswith which to query the state structure to identify the currentadvancement state, and the target advancement state. For example, forthe starting state, the APB may use the most current job information,e.g., the employer name, the title, and description describing thecurrent job, and query the state structure for states that most closelymatch current job information; for example, a select command may beperformed on the state structure for stats that most closely match allthe supplied terms, and use the highest ranking match as the selectedcurrent state. Similarly, the target job information may be used to finda target state.

Turning back to FIG. 17, upon establishing a start state and a targetstate 1714, the APB prepares to search for paths connecting the startand target states 1715, 316.

It should be noted that no target state need be selected, and in such aninstance, the APB will use the start state to query the state structurefor potential states that may be of interest to a seeker with noparticular target as will be discussed later in FIGS. 18-20 regardingiteration-wise implementations. Such iteration-wise implementationsallow a seeker to gauge and possibly forge their own pathways afterbeing presented with the various likelihoods of those adjacent andpotential advancement states.

Continuing with the description of a targeted implementation, it shouldbe noted that while the APB may make use of a start and target state,specification of intermediate states are also contemplated. However, itshould be noted that intermediate paths may be constructed by pair-wisere-processing of paths as discussed in FIG. 17; for example, if a seekerinitially chooses a start state of Janitor and target state of CEO, theAPB may construct a path of Janitor state→Manager state→CEO. However,seekers may themselves change and/or specify an intermediate state ofRegional Administrator. This intermediate state of RegionalAdministrator may be used by the APB as a target state with Janitorstate being the starting state; from which a first path may beconstructed as between the two states, e.g., Janitor state→FacilitiesAdministrator state→Regional Administrator state. In turn, the APB willthen use the target Regional Administrator state as a starting state andCEO as target state to construct a second path, e.g., RegionalAdministrator state→Regional Manager state→CEO state. Thereafter, theAPB may join the first resulting path together with the second resultingpath, as the intermediate Regional Administrator state is the same forboth paths, and result in a new seeker directed path, e.g., Janitorstate→Facilities Administrator state→Regional Administratorstate→Regional Manager state→CEO state. A practically limitless numberof pair-wise re-processing operations may be employed as a seeker seeksout and selects intermediate states for a path.

In preparing to search for connecting paths as between a start state andtarget state, the APB may use specified minimum likelihood thresholds,P_(min), and a maximum number of path state nodes N_(max) 1715. In oneembodiment, an administrator sets these values. In an alternativeembodiment, a seeker may be presented with a user interface where theyare allowed to specify these values; such an embodiment allows theseeker to tighten and/or loosen search constraints that will allow themto explore more “what if” advancement (e.g., career) advancement pathscenarios. The APB may then establish an iteration counter, “i”, andinitially set it to equal “1” 1716. Using the start state, the APB mayquery the state structure for the next most likely states 1717. In analternative path-dependent embodiment, the APB may use the seeker'sprovided experience information, i.e., the entire state path, as astarting point and query the state structure for next most likely statesfollowing the seeker's last experience state (more information aboutpath-dependant traversal may be seen in FIGS. 19 and 21).

As the state structure maintains the likelihood of moving from any onestate to another state, the APB may query for the top most likely nextstates having likelihoods greater than the specified minimum probabilityP_(min). For example, if a P_(min) is set to be 50% probability, i.e.,0.5, and the start state 1750 has the following partial list of relatednext states: state A with P=0.5 1751, state B with P=0.7 1752, state Cwith P=0.9, and state Z with P=0.1 1754; then of next states A, B, C andZ, only states A, B, and C have likelihoods above the P_(min) threshold,and as such, only those states will be provided to the APB 1717. In analternative embodiment, instead of specifying a likelihood threshold,P_(min) instead may specify the minimum number of results for the statestructure to return (e.g., P_(min) may be set to 10, such that the top10 next states are returned, regardless of likelihood/probability). TheAPB may then determine if any and/or enough matches resulted 1718 fromthe query 1717. If there are not enough (or any) matches that result1718, then the APB may decrease the P_(min) threshold by a specifiedamount (e.g., from 0.5 to 0.25, from 10 to 5, etc.); alternatively, theAPB (or a seeker) may want to try again 1729 by loosening constraints1731, or otherwise an error may be generated 1730 and provided to theAPB error handling component 1721.

If there are matching 1718 next states (e.g., A 1751, B 1752, C 1753)proximate to the start state 1750, then the APB may pursue the followinglogic, in turn, as to each of the matched next states (i.e., wherebyeach of the next states (e.g., A 1751, B 1752, C 1753) will form thebasis for alternative advancement paths (e.g., Path 1, 1791, Path 2,1792, Path 3 1793, respectively) 1733.

Upon identifying matching next states 1717, 1718, the APB may append1781 a next state (e.g., A 1751) 1722 to the start state. Upon appendinga next state to the start state 1722, the APB will then determine ifappended next state (e.g., A 1751) matches any of the target state(e.g., 1799) criteria 1723. In one embodiment this may be achieved bydetermining if the next state has the same state_ID as the target state.In an alternative embodiment, the state structure provides the staterecord of the target state to the APB, and the APB uses terms from thetarget state as query terms to match to the state record of the nextstate; when enough term commonality exists, the APB may establish thatthe next state is equivalent to, and/or close enough to the target stateto be considered a match.

If the appended next state 1722 does not match the target state 1723,then the APB will continue to seek out additional intermediate 1727state path nodes (e.g., D 1761 and F 1771) until it reaches the targetstate (e.g., 1799). In so doing, the APB will determine if the currentstate node path length “i” has exceeded the maximum specified state nodepath length N_(max) 1727. If not 1727, the current state node pathlength “i” is incremented by one 1728. Thereafter the last appendedstate (e.g., A 1751) will become the basis for which the query logic1717 may recur (i.e., the appended state effectively becomes thestarting state from which proximate nodes may be found by querying thestate structure as has already been discussed 1717) For example, in thisway next state A 1751 becomes appended to the start state 1750, and thenthe appended 1722 state A 1751 becomes a starting point for querying1717, where the state structure, may in turn, identify a state nodeproximate to the appended state, e.g., state D 1761; in this mannerstate D 1761 becomes the next state to state A 1751. By this recurrence1722, 1727, 1728, 1717, the APB grows the current path (e.g., Path 11791).

If the current state node path length “i” has exceeded the maximumspecified state path length, N_(max) 1727, then the APB may check to seeif there is another next state for which a path may be determined 1736.For example, if the maximum allowable state path length is set toN_(max)=2, and the APB has iterated 1728, 1717 to reach state F 1771along Path 1 1791, then the current state path length (i.e., totaling 3for each of states A 1751, D 1761, and F 1771) would exceed thespecified N_(max); in such a scenario where N_(max) has been exceeded1727, if the APB determines there are additional states next to thestart state 1736 (e.g., B 1752, C 1753), then the APB will pursue andbuild, in turn, a path stretching from each of the remaining next states(e.g., Path 2 1792 from next state B 1752, and Path 3 1793 from nextstate C 1753). If there is no next state 1736 (e.g., each of stats A1751, B 1752, and C 1753 have been appended to the start state 1750),the APB may then move on to determine if any paths have been constructedthat reached the target state 1737. If no paths reaching the target havebeen constructed 1737, then the APB (e.g., and/or the seeker) may wishto try again 1729 by loosening some of its constraints 1731. In oneembodiment, the maximum state path length N_(max) may be increased, orminimum likelihoods P_(min) may be lowered 1731 and the APB may onceagain attempt to find an advancement path 1716. If there is no attemptto try again 1729, the APB may generate an error 1730 that may be passedto a APB error handling component 1721, which in one embodiment mayreport that no paths leading to a target have been found.

However, if paths have been constructed 1737, then the APB may determinethe likelihoods of traversing each of the resulting paths 1724. Forexample, if we have a start state 1750 and a target state of 1799, theAPB may have found three states next to the start state with asufficient P_(min) (e.g., over 0.5); e.g., next states including: stateA with P=0.5 1751, B with P=0.7 1752, and state C with P=0.9 1753.Continuing this example, if the APB continues to search for statesproximate to each next state (as has already been discussed), it mayresult three different state paths: Path 1 1791, Path 2 1792, and Path 31793, all arriving at the target state 1799. Each of the paths may havea probability or likelihood of being reached from the start state 1750;in one embodiment, the likelihood may be calculated as the product ofthe likelihood of reaching each of the states along the path. Forexample, the Path 1 1791 calculation would be P_(A)*P_(D)*P_(F), (i.e.,0.5*0.9*0.9=0.405). Similarly, for Path 2 1792, the calculation would beP_(B)*P_(E) (i.e., 0.7*0.5=0.35). Similarly, for Path 3 1793, thecalculation would be P_(C)*P_(A)*P_(D)*P_(F), (i.e.,0.9*0.9*0.9*0.9=0.6561).

As such, the APB may determine the likelihoods for each of the pathsconnecting to the target state(s) 1724. Upon determining the pathlikelihoods 1724, the APB may then select path(s) in a number of manners1725. In the example three paths 1791, 1792, 1793, the most likely pathis Path 3 having a likelihood of 0.6561, the next most likely path isPath 1 having a likelihood of 0.405, and the least likely path is Path 2having a likelihood of 0.35. In one embodiment, the APB may select thepath having the greatest likelihood, e.g., Path 3 1791. In anotherembodiment, a threshold may be specified, such that the APB willprovide/present only the top paths over the threshold (e.g., if we usedP_(min) as the threshold and set it to 0.5, only Path 3 would beselected with its likelihood of 0.6561 exceeding that threshold). Inanother embodiment, all paths are presented to the user (e.g., in rankedorder) so that the seeker may explore each of the paths. Upon selecting1725 determined paths 1724, the APB may store the paths in memory,and/or otherwise return 1786 the resulting paths 1726 for further use bythe APB, e.g., provide the resulting paths for visualization to theseeker 1606, 1611 of FIG. 16.

Path-Independent Iteration-Wise APB

FIG. 18 is of a logic flow diagram illustrating iteration-wisepath-independent path construction embodiments for the APB. Uponobtaining seeker experience advancement information 1602 and determiningthat a iteration-wise independent advancement path is desired 1604 ofFIG. 16, the APB may use experience information to establish a startstate and identify suitable subsequent states for advancementconsideration 1832.

As has already been discussed in FIG. 17, in one embodiment, the seekerexperience information may be provided by the seeker by way of a webform as shown in FIGS. 24, 25, 26. Unlike in the targeted embodimentsdiscussed in FIG. 17 where a seeker may supply both a start state andtarget state 2528 of FIG. 25, a iteration-wise approach allows a seekerto identify a starting state in any number of ways as has already beendiscussed in FIG. 3 (e.g., identifying a category 2409, and zooming intoa related state 2412, and making selections 2414 of FIG. 24 to add aselected state to a path 2541, 2546 of FIG. 11; typing in a search term2424 to find matching states from a state structure 2426 of FIG. 24, andselecting those matching states to act as a starting state for a path;and/or the like).

In preparing to search for states proximate to a starting state, the APBmay obtain a starting state (e.g., from experience information, fromselection/indication obtained form a seeker via a user interface, and/orthe like) and use a specified minimum likelihood thresholds forconsidering proximate states P_(min) 1832, as has already been discussedabove. Upon obtaining a start state and a minimum likelihood 1832, aseeker may also provide state filter information 1834. In oneembodiment, state filter information may comprise: salary requirements,geographic region and/or location requirements, education requirements,relocation expense requirements, minimum occupational growth rates,expected demand levels for a state, and/or the like. This informationmay be supplied to the web interfaces discussed in FIGS. 24, 25, 26 andused as has already been discussed in FIG. 3. For example, additionalcriteria 2548 may be specified and supplied into text fields 2549. Inone embodiment, these attributes may stored in an attributes databasetable, that table may have a state_ID field that makes those attributesassociated with a particular state; as such the attributes may selectedby a state, and may be used as criteria for filtering. Although in oneembodiment, when selecting a state 2550 will show additional informationassociated with that state 2559, in an alternative embodiment, uponindicating that filtering should be used 2548, a user is able to placefiltering criteria into fields 2549 of FIG. 25 that will be made part ofthe query to the state structure, which may have an associatedattributes database, and such filtering criteria will be used to filterout unwanted states. These filter criteria may be part of the XML querystructure as has already been described in FIG. 3. Upon obtaining astart state and minimum threshold 1832 and filter information 1836, aquery is provided to the state structure and any associated attributedatabase 1838. The APB then obtains states next states proximate to thestarting state having a minimum likelihood threshold and whoseassociated attribute information also satisfies the requirements of thesupplied filter selections 1838. In another embodiment, a threshold maybe based upon minimum likelihood and maximum number of results. If thereare no matches 1840, the seeker may adjust the starting point andminimum thresholds and attempt to identify next states again 1832. Inanother embodiment, an error may be generated indicating no matches1842. If there are matching states 1840, in one embodiment, thosematching states 1840 may be appended to the starting state and made apart of the advancement path 1844. Those matching next states 1840 maythen be displayed 1846. It should be noted that when making a selectionof a state 2550, and supplying any filter criteria 2559, the APB mayobtain matching 1840 states that may be tenuously appended as potentialnext states 2560 of FIG. 25. Seekers may make such appending morepermanent by indicting they would like to add a state to a path they areconstructing 2546, 2543 of FIG. 25, which may result in the updatingand/or modification of the path depiction that is displayed 1846. Uponupdating the display 1846, the APB may allow a seeker to continue onfrom the last selected/added state and iterate and continue to build adesired path further 1832; otherwise operations may return to FIG. 161686. It should be noted in one embodiment, this path-dependantiteration-wise mechanism may be use to select intermediate states in thetargeted path-dependant mechanism described in FIG. 17.

Path-Dependent Iteration-Wise APB

FIG. 19 is of a logic flow diagram illustrating iteration-wisepath-dependent path construction embodiments for the APB. This is analternative path-dependant embodiment of FIG. 22. Upon obtaining seekerexperience advancement information 1602 and determining that aiteration-wise independent advancement path is desired 1604 of FIG. 16,the APB may use experience information to establish a start state andidentify suitable subsequent states for advancement consideration 1832.

As has already been discussed in FIGS. 17 and 18, in one embodiment, theseeker experience information may be provided by the seeker by way of aweb interfaces as shown in FIGS. 24, 25, 26. Unlike in the targetedembodiments discussed in FIG. 17 where a seeker may supply both a startstate and target state 2528 of FIG. 25, a iteration-wise approach allowsa seeker to identify a starting state in any number of ways as hasalready been discussed in FIG. 17. In an alternative embodiment, the APBtake into account all the seeker's experience information. While in FIG.18, examples were provided where a single experience state was providedand/or otherwise selected by the seeker, however, in this path-dependantembodiment, a seeker's full experience information may used as a basisof path discovery. Some seekers may have no experience history or asingle entry, and in such instances, this path-dependant embodiment willlook much like that path-independent embodiment. In one embodiment, aseeker may supply this experience information into structured web forms,which may be stored as structured data in a seeker profile associatedwith the seeker (e.g., a seeker may enter their resume job experiencesinto a web form). In an alternative embodiment, a seeker may providetheir resume, which in turn may be parsed into structured data, theresulting structured data serving as experience information.

In preparing to search for states proximate to a path-dependent startingstate, the APB may use a specified minimum likelihood threshold forconsidering a state proximate to the latest state in their experienceinformation P_(min) 1950. In one embodiment, a seeker may supplyexperience information, which will serve as path-dependant (“PD”)criteria 1952, which as described in FIG. 17 (for example a statestructure as may be represented, in one embodiment, by way of the XMLstructure), may include a temporal sequence and description ofadvancement progression (e.g., jobs 1, jobs 2, etc.). The APB maydetermine a state for each of these advancement progression entries andcrate a path describing the seeker's past state path progression and usethat path as a basis to search the state structure (e.g., as has beendescribed above and in greater detail in patent application Ser. No.12/427,736 filed Apr. 21, 2009, entitled “APPARATUSES, METHODS ANDSYSTEMS FOR A CAREER STATISTICAL ENGINE,”); the last progression entry(e.g., the latest job held by a seeker) may be used as a basis fromwhich the seeker will further build out their advancement (e.g., career)path. In one embodiment, the state structure may return state, which maybe used by the APB as state advancement experience information. Forexample, the job entries (e.g., Job 1, Job 2, etc.) from the structured(e.g., XML) advancement experience information in FIG. 17 may besupplied to the state structure, which in turn may return equivalent jobstates. Instead of using the FIG. 17 advancement experience information,a state version of that information may be used by the APB, for example:

<State Advancement Experience Information ID=“experience12345”>

<Experience Information>

-   -   <State ID 1>111111</State ID 1>    -   <State ID 2>222222</State ID 2>    -   <State ID 3>333333</State ID 3>

</Experience Information>

<Advancement Information>

-   -   <DescriptionTerms>        -   <Term1>Executive </Term1>        -   <Term2>Consultant </Term2>    -   </DescriptionTerms>

</Advancement Information>

<Filter Information>

-   -   <DescriptionTerms>        -   <Filter1>Salary >$100,000</Filter1>        -   <Filter2>Region Zipcode [e.g., 10112]<25 miles </Filter2>        -   <Filter3>Degree <Masters </Filter3>        -   <Filter4>Growth >20%</Filter4>        -   <Filter5>Relocation Expenses=TRUE </Filter5>        -   <Filter6>Expected Next Year Occupation Demand Level>20,000            jobs </Filter6>        -   <Filter7>Signing Bonus >$10,000</Filter7>        -   <Filter8>Annual Technology Stipend >$5,000</Filter8>        -   <Filter9>Annual Health Insurance Stipend >$25,000</Filter9>        -   <Filter10>Regular Travel=False </Filter10>        -   <Filter11>Salary Level >[Top] 10% [in field]</Filter11>    -   </DescriptionTerms>        </State Advancement Experience Information ID>

In the above state version of advancement experience, the statestructure provided state equivalents of the job entries in the FIG. 17experience information, and this state experience information may besupplied to the state structure as a path comprising State ID 1, StateID 2, and State ID 3 representing Job 1, Job 2 and Job 3 from the XMLdescription in FIG. 17. Results from querying the state structure withan existing state progression path will provide the APB and use thelatest advancement progression entry as a starting point; e.g., from theabove state advancement experience information, State ID 3 would be thestate from which a further advancement path would be build by the APB,i.e., State ID 3 would be the path-dependant start state to whichadditional path advancement states would be appended 1952.

Upon populating the APB with path-dependant criteria (e.g., withexperience advancement experience information, state advancementexperience information, and/or the like) 1952 and obtaining a minimumlikelihood threshold 1950, a seeker may also provide state filterinformation 1954, which may be used to modify the path-dependentcriteria 1954 (as has already been discussed in FIG. 18). In oneembodiment, state filter information may comprise: salary requirements,geographic region and/or location requirements, education requirements,relocation expense requirements, minimum occupational growth rates,expected demand levels for a state, and/or the like. These filtercriteria may be part of the XML query structure as has already beendescribed in FIG. 17.

Upon obtaining a minimum threshold 1950, populating the APB withpath-dependant criteria 1952 and filter information 1954, a query may beprovided to the state structure and any associated attribute database1956. For example, the state advancement experience information (orsubset thereof) may be provided to the state structure as a query. Uponobtaining query results from the state structure, the APB may determinewhich of the returned states to use that satisfy the filter selections1954 and minimum thresholds specified and retrieve the state records(and any associated attributes) related to the determined state(s) 1956.The APB may then determine if any state results match 1958; if not, theseeker may adjust the parameters of the search by starting over 1950, oralternatively an error is generated 1959.

If there are matching states 1958, in one embodiment, those matchingstates 1958 may be appended to the path-dependant starting state andmade a part of the advancement path 1960. Those matching next states1958 may then be displayed 1961. It should be noted that when making aselection of a state 2550, and supplying any filter criteria 2559, theAPB may obtain matching 1958 states that may be visually appended aspotential next states 2560 of FIG. 25, providing highlighting to showpotential path connections. Seekers may make such appending appear morepermanent 1963 by indicting they would like to add a state to a paththey are constructing 2546, 2543 of FIG. 25, which may result in theupdating and/or modification of the associations between states andgeneration of a path depiction that is displayed 1961. Upon updating thedisplay 1961, the APB may allow a seeker to continue 1962 on from thelast selected/added state 1960. If no continuation is desired or needed,operations may return to FIG. 16 1986. Otherwise, if continuation isdesired 1962, the APB may allow a user to update their previousexperience information 1963, 1964. If a user wishes to append or addstates representing past experience (e.g., if the seeker did notinitially supply all of their experience information as path dependentcriteria 1952) 1964 and specifies such, the APB will allow them toappend such experience states as path-dependence criteria 1952. In onenon-limiting example, a seeker may build up 2546, 2543 of FIG. 25 statesrepresenting their experiences in this manner. Alternatively, the seekermay not wish to append experience states 1963, yet the APB may determineif any changes to any of the path-dependence criteria was affected bythe seeker (e.g., a seeker may have changed an originally suppliedexperience state to another, the other perhaps showing a promotion intheir most current work employment) 1965. If no changes topath-dependant criteria were determined 1965, the APB may continue toiterate and build a path based on the last appended state 1960, 1956.However, if there has been a change in the path dependant criteria 1965,this changed criteria will form the basis of iterated path dependentcriteria 1952.

Path-Independent N-Part Open-Ended APB

FIG. 20 is of a logic flow diagram illustrating N-part path-independentpath construction embodiments for the APB. This is an alternativepath-independent open-ended embodiment of FIG. 18. Upon obtaining seekerexperience advancement information 202 and determining that aiteration-wise independent advancement path is desired 204 of FIG. 2,the APB may use experience information to establish a start state andidentify suitable subsequent states for advancement consideration 2065.

As has already been discussed in FIGS. 17, 18 and 19, in one embodiment,the seeker experience information may be provided by the seeker by wayof a web form as shown in FIGS. 24, 25, 26. Unlike in the iteration-wiseembodiments discussed in FIG. 4, an open-ended approach allows a seekerto identify a starting state in any number of ways as has already beendiscussed in FIG. 17; it also allows the seeker to specify desired pathlength N. As such, the APB, an administrator, another system, or theseeker may specify the desired number of states to comprise anadvancement path, that length being “N” 2065.

In preparing to search for states proximate to a starting state, the APBmay obtain a starting state (e.g., from experience information, fromselection/indication obtained form a seeker via a user interface, and/orthe like) and use the specified path length limit N, as has already beendiscussed above. Upon obtaining a start state and limit 2065, a seekermay also provide state filter information 2067. In one embodiment, statefilter information may comprise: salary requirements, geographic regionand/or location requirements, education requirements, relocation expenserequirements, minimum occupational growth rates, expected demand levelsfor a state, and/or the like. This information may be supplied to theweb interface discussed in FIGS. 24, 25, 26 and used as has already beendiscussed in FIG. 17. For example, additional criteria 2548 may bespecified and supplied into text fields 2549. Although in oneembodiment, when selecting a state 2550 will show additional informationassociated with that state 2559, in an alternative embodiment, uponindicating that filtering should be used 2548, a user is able to placefiltering criteria into fields 2549 of FIG. 25 that will be made part ofthe query to the state structure, which may have an associatedattributes database, and such filtering criteria will be used to filterout unwanted states. In another embodiment, browsing an associatedhierarchy through nested pop-up menus 3030 of FIG. 30 or traversal andselection of nodes in a topography 2422, 2427 of FIG. 24 provide anothermechanism for identifying states and building paths. These filtercriteria may be part of the XML query structure as has already beendescribed in FIG. 17. Upon obtaining a path limit and filter information2068, the APB may set the current path length “i” to equal “1” 2069. TheAPB may then provide a query to the state structure and any associatedattribute database 2070, including filter criteria, as has already beendiscussed. The APB then obtains states next states proximate to thestarting state having a minimum likelihood threshold and whoseassociated attribute information also satisfies the requirements of thesupplied filter selections, and may append those next states to thecurrent advancement path 2071. I an alternative embodiment, a seeker maytraverse by categorical hierarchy selections as show in 3035 of FIG. 30,whereby a seeker can iteratively make state selections by identifyingstates through hierarchical selections. Thereafter, the APB mayincrement the path length counter “i” by one 2071 to track the growth ofthe path length resulting from the appending 2070. If the maximum pathlength N has not been reached 2073, the APB may iterate and similarlyconduct queries on the appended next states, to extend the path 2070. Ifthe path length has been reached 2073, operations may return to FIG. 162086.

Path-Independent N-Part Open-Ended APB

FIG. 21 is of a logic flow diagram illustrating N-part path-dependentpath construction embodiments for the APB. This is an alternativepath-dependent open-ended embodiment of FIG. 19. Upon obtaining seekerexperience advancement information 1602 and determining that aniteration-wise independent advancement path is desired 1604 of FIG. 16,the APB may use experience information to establish a start state andidentify suitable subsequent states for advancement consideration 2174.

As has already been discussed in FIGS. 17, 18, 19 and 20, in oneembodiment, the seeker experience information may be provided by theseeker by way of a web form as shown in FIGS. 24, 25, 26. Unlike in theiteration-wise embodiments discussed in FIG. 19, an open-ended approachallows a seeker to identify a starting state in any number of ways ashas already been discussed in FIG. 17; it also allows the seeker tospecify desired path length N. As such, the APB, an administrator,another system, or the seeker may specify the desired number of statesto comprise an advancement path, that length being “N” 2174. While inFIG. 20, examples were provided where a single experience state wasprovided and/or otherwise selected by the seeker, however, in thispath-dependant embodiment, a seeker's full experience information mayused as a basis of path discovery. Some seekers may have no experiencehistory or a single entry, and in such instances, this path-dependantembodiment will look much like that path-independent embodiment. In oneembodiment, a seeker may supply this experience information into webstructured web forms, which may be stored as structured data in a seekerprofile associated with the seeker (e.g., a seeker may enter theirresume job experiences into a web form). In an alternative embodiment, aseeker may provide their resume, which in turn may be parsed intostructured data, the resulting structured data serving as experienceinformation.

In preparing to search for states proximate to a path-dependent startingstate, the APB may discern a path-dependent starting state as hasalready been discussed in FIG. 5, and use the specified path lengthlimit N 2174, as has already been discussed above. Upon obtaining a pathlimit N, the APB may set the current path length “i” to equal “1” 2175.A seeker may then provide state filter information 2176, which may beused to obtain resulting states matching the filter criteria 2177. Inone embodiment, state filter information may comprise: salaryrequirements, geographic region and/or location requirements, educationrequirements, relocation expense requirements, minimum occupationalgrowth rates, expected demand levels for a state, and/or the like. Thisinformation may be supplied to the web forms discussed in FIGS. 24, 25,26 and used as has already been discussed in FIG. 17. For example,additional criteria 2548 may be specified and supplied into text fields2549. Although in one embodiment, when selecting a state 2550 will showadditional information associated with that state 2559, in analternative embodiment, upon indicating that filtering should be used2548, a user is able to place filtering criteria into fields 2549 ofFIG. 25 that will be made part of the query to the state structure,which may have an associated attributes database, and such filteringcriteria will be used to filter out unwanted states. These filtercriteria may be part of the XML query structure as has already beendescribed in FIG. 17. Upon obtaining a path limit 2174 and filterinformation 2176 and obtaining the filtered states 2177, a seeker maysupply experience information, which will serve as path-dependantcriteria 2178 as has already been described in of FIG. 19, and in FIG.17. The APB will then return states matching the aforementioned criteriaand may then create associations between states that appear to appendthose matching next states the path-dependant starting state, which arethereby made a part of the associated states representing theadvancement path 2180.

Seekers may make such appending 2180 more permanent by indicting 2181they would like to add a state to a path they are constructing 2546,2543 of FIG. 25. As such, the APB may allow a user to update theirprevious experience information 2181, 2182. If a user wishes to appendor add states representing past experience (e.g., if the seeker did notinitially supply all of their experience information as path dependent2178) 2182 and specifies such, the APB will allow them to append suchexperience states as path-dependence criteria 2182. In one non-limitingexample, a seeker may build up 2546, 2543 of FIG. 25 states representingtheir experiences in this manner. Alternatively, the seeker may not wishto append experience states 2181, and then the APB may increment thepath length by one 2183 to indicate that the current path has grown byone. Upon incrementing the current state, it may result in the updatingand/or modification of the path depiction that is displayed 2185. Uponupdating the display 2185, the APB may determine if the maximum pathlength N is less than the current path length i; if the current lengthof “i” is longer, then operations may return 2186 to FIG. 16 1986.Otherwise 2184, the APB may continue to grow to set lengths N byiterating 2179.

Path Gap Analysis

FIG. 22 is of a logic flow diagram illustrating gap analysis embodimentsfor the APB. In one embodiment, a seeker may access the APB (e.g.,either anonymously, be logged into the system, and/or the like) 2285. Inso doing, the seeker may provide the APB with a start state 2286 andtarget state 2287, as has already been discussed in FIG. 21. The APB mayalso populate additional states (e.g., B, C . . . N) in an embodimentthat allows for multi-segment gap analysis. As such, the APB willdetermine if it needs to analyze across multiple pat states 2208, and ifso, it will add those states for analysis and subsequent iteration 2209,otherwise 2208, the APB will continue by initiating querying 2288. TheAPB may use the start and target states as a basis to query the targetstate structure to discern states proximate to the target state 2288. Inan alternative embodiment, the APB may supply an intermediate targetstate; this may be achieved by first specifying a start state and anintermediate target state and generating a path therebetween as alreadydiscussed; thereafter another path is generated as between theintermediate target state being supplied as a starting state andspecifying a final target state, and once again determining a paththerebetween; the two paths being connected. If the APB does not obtainany matches 2289, the seeker will be afforded an opportunity to restart2286, and/or alternatively an error message may be generated 2290. Ifthe APB does identify matches 2289, then the APB may query the featureattributes and transition values as stored in an attributes tableoccurring in time between the start and next state (e.g., as between Aand B, then B and C, and N−1 and N; N being the target state) 2290; whenthere are only two states, there is just a start state and target state.For example, the APB may query an attributes database with attributesassociated with states from the state structure (e.g., as in theadvancement taxonomy), wherein the attributes maintain informationdifferences in salary as between different individuals having the sameemployment state. As such, the APB examines the attributes databaserecord entries associated with each state and determine the common gapattributes; in another embodiment, an administrator specifies whichattribute types have the most common gap attributes. Thereafter, the APBmay calculate the feature gaps 2291 as discussed in greater detail inFIG. 23. Similarly to the previous query for features 2290, the APB mayquery for state change indicators as between states (e.g., as betweenABi, BCi . . . N−1Ni) 2292. Similarly to the feature gap calculation,the APB may calculate the state change indicators 2299 as discussed ingreater detail in FIG. 23. In one embodiment, the APB may determine ifthese calculated gaps are statistically significant 2293 (e.g., bydetermining comparing it the value exceeds a common standard deviationfor the gap type). If these are statistically significant, then the APBmay return these gap feature attributes and transition indicators (e.g.,as discussed in greater detail in FIG. 23), which may be provided to theseekers (e.g., to see how their salary compares to others living in thesame region and having the same vocational post) 2296; in oneembodiment, this gap analysis may be employed by APB for benchmarking.If there is no statistical significance 2293, then an error message maybe returned, noting that no significant gap attributes exist 2294 andthe APB will allow a user to try 2295 and recurse 2286 if the seeker sowhishes 2295, otherwise gap analysis operations cease. In oneembodiment, such gap analysis may be instantiated when user selectsadditional options 2647, 2666 of FIG. 26 after having selected start andtarget states.

FIG. 23 is of a state path topography transition diagram illustratinggap analysis embodiments for the APB. In one embodiment, a gap analysismay be determined as between start state A 2310 and target state C 2301,having one intermediate state B 2305. In such an example, each state hasa gap measurable attributes represented by F 2341, 2351, 2361, which maybe float, integer, textual, and/or functions that represent storedattributes in an attributes database table. The features may bequalitative and/or quantitative. Features may include skills topics,terms, attributes (e.g., salary, vacation, etc.) and/or the like. Gaptransition indicators may also represent part of a transition from onestate to another. Transition indicators may include years of experience,obtainment of education (e.g., obtaining a new degree), attributes,and/or the like. In one example, state A (e.g., a state representing anassistant graphics designer position) have a set 2320 of attributes F2361 having a salary of $50,000 2363 and a skill of using MicrosoftPaint 2364. State B (e.g., a state representing a graphics designerposition) may have a set of attributes F 2351, having a salary of$60,000 2353, a skill set of Microsoft Paint, Photoshop, and TeamManagement skills 2354, and provide 20 days of vacation time 2356. Also,in this example, state C (e.g., a state representing a managing graphicsdesigner position) may have a set of attributes F 2341 having a salaryof $75,000 2343, and skill requirements of Team Management,Administration 246.

A state may have a certain set of attributes associated with it that isexclusive to that state. The difference between state A and B, in oneembodiment, may be represented as the features of B subtract out thesame duplicative features of A. For example, there is a $10,000difference in salary as between state A 2310, 2363 and state B 2305,2353. There also are indicators driving people from state A to B, e.g.,like years of experience, the obtainment of a specific degree, and orthe like. In one example it make take 5 years of experience for atransition to occur on average, e.g., AB_(i)=5 2307, BC_(i)=5 2303;these are indicators of change between states. As such, in oneembodiment gap indicators as between states A and B may be calculated asfollows:G _(i(A→B)) =B _(F) −A _(F) +AB _(i) 2399, or

Salary: $10,000; Skill: Photoshop, Team Management; 5 years transition,

As such, gap indicators as between states B 2305 and C 2301 may becalculated as follows:G _(i(B→C)) =C _(F) −B _(F) +BC _(i) 2398, or

Salary: $15,000; Skill: Administration, -Paint, -Photoshop; 5 yearstransition and a Masters Degree.

The gap analysis may work as an additive between any two states. So thestate change drivers takes drivers of change between two states andidentifies them as subtractive features as well as indicators. As aconsequence, gap indicators as between state A 2310 and C 2301 may becalculated as follows:G _(i(A→C)) =C _(F) −B _(F) −A _(F) +AB _(i) +BC _(i) 2397, or

Salary: $25,000; Skill: Team Management, Administration, -Paint; 10years transition and a Masters Degree.

Interaction Displays

FIGS. 24-26 are of a screen shot diagram illustrating embodiments forthe APB. In FIGS. 24A-24G, the entry screen to the APB is shown 2401;however it should be noted that the APB may be used without having auser profile or account. In one embodiment, a seeker may initiallyinteract with a state topology overview 2405, which may also have panelsfor accepting inputs from the seeker to search out states, and an areato show information and results from inputs provided to the APB 2407. Inone embodiment, a seeker may move their cursor to an advancement (e.g.,career) category displayed in the state topology overview and make aselection 2409. Upon selecting the category and/or state, the displayarea 2409 will focus in on the item selected 2412 and may provide theseeker with options (e.g., start/add to my path, get details about thisstate, find a job based on this state) 2414. It should also be notedthat state paths may be represented in numerous ways; e.g., while insome figures the state paths are depicted as interconnected nodes on agraph topography 2555, 2544 of FIGS. 25E and 25F, respectively, inanother embodiment the path may be represented as a series of numberedboxes 2547 of FIG. 25G having information relative to each statedisplayed within. A seeker may select to view the state path 2545 ofFIG. 25 by providing indications that they wish to visualize the statepath differently. In one embodiment, a template architecture iscontemplated where numerous visual depictions are available and use theunderlying state path and state structure topology as data constructsonto which the templates may be mapped. Further, the APB may provide akey showing what the various weight lines 2411 signify as connectorsbetween (e.g., circular) nodes. For example, if a seeker selected anoption to “get details” 2416, the APB may display attribute detailinformation about the selected state in a information display panel2418, which may include the number of people in this job (both currentand projected), expected rate of growth for the state, and otherassociated attribute information. The seeker may navigate about thetopology by selecting a scale slider 2420 which will allow the seeker tozoom out and see more of the topology 2422 (or conversely, to zoom inand see more detail; panning and other arrangement options may also beemployed).

In another embodiment, a seeker may enter a search term they believerelates to a (e.g., career) state they have an interest in 2424, whichwill result in the APB showing its top matches 2426 in the informationpanel as well as highlighting relevant identified experience states inthe topology itself 2427. It should be noted that in one embodiment, thetopography will adjust its overall view (e.g., zoom level) to show thepath results, and when making selections of states, the topography willtraverse and provide a fly-by depiction of the topography on to toselected states.

In FIGS. 25A-25G, the APB allows a seeker to provide both start andtarget query terms 1124 (as well as additional options 2548 that wouldallow the seeker to provide additional attribute filter criteria). Inone embodiment, the additional filter criteria may be entered in the APBinformation panel 2549 and be used as part of the basis of a query tofind matches in a state structure. As a seeker types 2526, suggestedterms and topics are displayed 2526. If a state is selected 2550, otherrelated states are pointed to and highlighted 2560. In an alternativeembodiment, upon providing and determining start and target states 2528,2530, 2532, the APB may determining a path with intermediary states(e.g., 2531) as between the start and target states 2533; and providethe seeker with the ability to choose as between multiple paths thathave differing numbers of intermediary states and likelihoods ofattainment by the seeker 2535. For example, in one embodiment a seekermay make selections 2537 as among the various available paths betweenthe start and target states, and the topology display area will beupdated to reflect the different paths 2539. It should be noted that theAPB allows a seeker to either build up or modify a path. In oneembodiment this may be achieved by selecting adjacent states 2546 tostates in a path 2555 and making selections to add the selected state tothe path 2543. In one embodiment, a path list 2544 may show the currentset of states that comprises the current path such that a seeker may beapprised of the current path even if it is not fully visible in thetopography display area.

In FIGS. 26A-26H, the APB allows a seeker to vary visualizations innumerous ways. For example, a user may elect to vary the visualdepiction of the display topology by engaging an option widget 2643,which may in turn show a dialogue box that allows the user to turnon/off the ability to show, e.g., common next states, show most commonpaths, show tips, show trace, and/or the like 2645. For example,selecting the show tips option will highlight potential and/or likelynext states for consideration for a seeker. In another embodiment, ifthe seeker selects show trace, a breadcrumb trail of their path ishighlighted. Another option is “find jobs in path,” which when selectedwill allow a seeker to apply for one or more jobs that are identifiedalong a constructed path. In another view upon selecting a state, theuser may be presented with options to find a job and inform the userthat the state is a common path state 2647, 2666. Another embodimentshows that upon selecting a “find a job” option 2647, the user may bepresented with job listings 2649 and sponsored ads 2651. In oneembodiment, occupational profile tags may be used with sponsored ads. Inone embodiment, profile ad tags may be called as follows:

http://ads.monster.com/html.ng/site=mons&affiliate=mons&app=op&size=728x90&pp=1&opid=****&path=(DynamicPathValue)&dcpc=#####&ge=#&dcel=#&moc=######&dccl=##&mil=#&state=##&tile=

http://ads.monster.com/html.ng/site=mons&affiliate=mons&app=op&size=300x250&pp=1&opid=****&path=(DynamicPathValue)&dcpc=#####&ge=#&dcel=#&moc=######&dccl=##&mil=#&state=##&tile=

http://ads.monster.com/html.ng/site=mons&affiliate=mons&app=op&size=(TBD)&pp=1&opid=****&path=(DynamicPathValue)&dcpc=#####&ge=#&dcel=#&moc=######&dccl=##&mil=#&state=##&tile=

#####: These values are set by the user's cookie.

In another example embodiment, in FIG. 27, job listings may be presentedas part of a job carousel, as illustrated, where a user may view joblistings 2733 in a “lazy susan” format 2793; the user may spin jobwidgets 2733 to the left 2791 or right 2792 by click-dragging orselecting “spin” arrows 2791, 2792 and rotating jobs out of view 2744,2755. For example, if a user clicks on the left spin arrow 2791, joblisting 0 2944 will spin into view and job listing 4 2766 will spin outof view. It should also be noted that the job carousel may also be usedfor job ads outside the context of the APB; in one embodiment, a usermay click on a job listing 2777 and an apply for job menu/button/widgetmay appear 2793, which would allow a user to move on to apply for thejob. In an alternative embodiment, the APB may prompt or otherwiserequest user identifying information 2794 (e.g., a unique identifier, auser name and password, a cookie having same, and/or the like), whichmay be used to obtain a seeker's experience information and begin a jobapplication process. Moving back to FIG. 26, it should be noted that aseeker may engage and save a desired path to their profile 2665. Also,it should be noted that a user may select a tab pane that providesinformation to help a seeker advance/continue their education, includingeducational listings and ad space 2653. The APB also provides theopportunity for the seeker to select path segments 2655, which in turnwill provide the seeker with opportunity to select a path, overwrite thepath, modify the path, and/or the like 2657, 2659.

Interaction Interface Component

FIG. 28 is a logic flow diagram illustrating embodiments for invokingand displaying an APB. In one embodiment, the APB may be manifested in aweb environment as discussed throughout in FIGS. 24-26. In one suchembodiment, the APB may be supplied, in one example embodiment, asFlex-based Shockwave Flash (SWFs) that interact with the CSM via a webservices layer and with the parent through Javascript-based callbacks,which may be delivered via a web page served by an information serverfor use by seeker clients. Once the web page is loaded in a web client,the interface may be instantiated and connect with the APB, itsdatabase, its component collection, and in one embodiment, through theinformation server. Upon instantiation of the web page, presentation andoperation of the interface may start by querying the seeker's web clientto obtain display environment and browser capabilities information: thismay include the type of browser, cookies (with account information, ifany), plug-in capabilities (if any), Javascript support, Java support,version numbers thereof, screen resolution and dimensions, window sizes,and/or the like. In one embodiment, this may be done with anHTTPBrowserCapablities Request.Browser inquiry via a Microsoft .Netobject call. The query may occur, and the seeker may access the APB bylogging into an account (or by determining that a cookie containsidentifying information allowing for login) or anonymously. Thereafter,the seeker's client may provide and the APB may obtain a user's systeminformation, and if logged in and otherwise unsaved, this informationmay be stored in a profile database table 2803. The APB may then providean interface appropriate to the seeker's client. In one embodiment,templates such as cascading style sheets, HTML templates, and/or thelike may be supplied providing the seeker with a preliminary career pathinteraction interface 2805. In another embodiment, Flash-based contentrendering may be used. In one embodiment, a seeker may select an initialdisplay type 2807; for example, a seeker may select a topography basedinterface 2544 of FIG. 25, linear information view 2547, a straight-linelist view 2544, a nodal path view 2533, and/or other views 2807. Uponproviding such indication, the appropriate and selected view may beprovided to and loaded by the seeker's web client and set 2809; inloading the template the appropriate interaction interface widgets areloaded.

Upon loading the template interface view 2809, the APB may then begingenerating a representation of a given path for display in accordancewith a given APB interaction interface template. For each noderepresenting a state to be rendered to display 2811, the APB may query aAPB database for seeker advancement states 2813. In one embodiment, thismay be a seeker's advancement experience information. In anotherembodiment, it may be a clean state with no state topography, where aseeker may begin searches for job states, as has already been discussedearlier in FIGS. 24-26. Upon obtaining states from the APB, the statemay be loaded into the interface element representing the state 2814.For example, in a topography map, a user's start state may be loaded asstarting node in that topography. In one embodiment, a user may filterresults 2815; the filters may be defined by the APB or the seeker. Inone embodiment, a user may, for example, specify likelihood thresholds,salary levels, and/or the like. So for example, if a user starts with ablank topography and performs a search for a starting state, the usermay specify filter attributes, which may be supplied as SQL queryselects, and which will act to narrow the returned states. The APB maycontinue to iterate if there are additional interface widgets that needto be put into effect 2816, 2811, otherwise, the APB may then move on todetermine the type of analysis to be used for path determination 2817.In one embodiment, the APB may allow a seeker to examine pathsindependent of their own advancement experiences in a path-independentapproach, in a path dependant approach, perform gap analysis, examineseeker's status at a given state relative to the aggregate (e.g.,comparing the aggregate salary for a state to the seeker's salary at agiven state), and/or the like 2817. Upon having obtained an initial setof seeker states 2811, and selecting an analysis type 2817, the APB mayperform a next-state and/or path determination analysis by usingselected states as starting and/or target states and using that to querythe state structure 2819. Upon obtaining analysis results 2819, the APBmay provide user interface elements along with widgets representingstates for display on the seeker's client 2812. Upon displaying thepathing interface 2821, the APB may monitor for seeker interaction withthe pathing interface and/or for further information 2823, as hasalready been discussed with regard to FIGS. 24-26. If there is nointeraction 2825, then the APB may continue to cycle looking foradditional input 2823. If there is seeker interaction 2825, then the APBmay check if a new display set type has been requested 2826; forexample, if a user selects to view a path linearly 2545 instead of as atopography 2543 of FIG. 25. If a seeker elects to change the display settype 2826, then the APB will load in a corresponding template throughwhich reimaging of the display may continue. If there is no indicationof changing the display type 2826, then the APB may determine if theinteraction interface is being dismissed and/or terminated; if so,termination will ensue and execution will come to an end. Otherwise, theAPB will go on to determine if what changes need to take place 2829 as aresult of user interaction 2825. For example, if a widget node isright-clicked upon, or other selection indicia is provided where aselection indicator (e.g., a cursor) intersects with a widget (e.g., anode representing an experience state), then the APB may determine if itneeds to query its CSE component 2831. For example, if a user decides tochange an experience path by adding a state to the path, changing one,and/or the like 2541, 2543 of FIG. 25, then the APB would need to obtainupdated state and/or path information by querying the CSE component forupdated data 2833. If no query is required 2831 or upon obtaining theresults form the query, the APB will determine if re-drawing the displayis required 2835. If no re-draw is required, the APB may continue tomonitor for user input. If updates are required, e.g., to account forupdated state information obtained from the state structure 2831, thenthe interface may be re-drawn to account for the update 2837, and thenthe APB may continue to monitor for interactions 2823.

FIG. 29 is a logic flow diagram illustrating embodiments for trackingseeker interactions with a APB. In one embodiment, upon initiatinginteractions with the APB, e.g., as in FIG. 29, the APB may track seekerinteractions by associating feedback widgets with elements ofadvancement experience information 2952. Momentarily moving to FIG. 30,it is of a block diagram illustrating feed back widgets for interactionswith a APB. As has already been discussed, in one embodiment a seekermay provide a title, keywords, and/or the like to the APB 3001 and lookfor a match 3003. Such a search will result in search results that maybe displayed and/or highlighted by the APB in a number of ways,including a list 3005, 2424, 2426, 2427 of FIG. 24. In one embodiment,seekers may provide feedback in a number of ways. In one embodiment, theseeker may inform the APB that search results are not appropriatematches 3010, in which case the seeker may specify a job state manually3030. In one embodiment this may be achieved by allowing a seeker tonavigate a hierarchical topic structure wherein various tiers representtop level topics and related nested topics. In one such embodiment, aseeker may make such selections by using pop-up menus that are populatedfrom the structure model's topic tables. In one embodiment, the XMLtopic model structure may be used. Once selecting an appropriateadvancement, e.g., job identifier, 3030 or selecting matches 3005 andasking to confirm either setting 3015, 3035, a feedback widget may bepresented 3020 in the information inspection area of a APB interface,e.g., 2407, 2418 of FIG. 24. In one embodiment, a slider widget 3020 maybe employed, wherein the seeker may move the slider to represent a rangeof a good match 1.0 to a poor match 0.0 by position of a slider 3020;that value may then be selected 3025 and stored in the APB. The ratingsmay be for all kinds of attributes, for example, instead of a confidencerating for the search results, a user may opt to “rate this job” 2543 ofFIG. 25 by right-clicking on an experienced state and be presented witha rating widget 3040 where they may confirm how bad or good anexperience was 3040, 3045. Similarly, a user may decide to providecomments about a job or experience through a text box 3050, the contentsof which may be saved to the APB database. In addition, users may rate3060 the comments and submit those to the APB. Moving back to FIG. 29,various other feed back attributes may be employed and/or stored, suchas: text boxes to allow seekers to provide comments regarding a selectedstate, job satisfaction ratings for experienced states (e.g., usingsliders), experience requirements (e.g., using toggles and/or text boxesto obtain additional requirements), and/or the like 2952.

Upon associating feedback with topic and/or job related information2952, the APB may then track the users view and interaction with anygiven job profile 2954. In one embodiment, tracking may take place bydoing the following for each viewing of a job/advancement profile by aseeker 2956. For each such interaction by a seeker with a job profile2956, the APB may load in an appropriate feedback widget when a jobprofile is loaded for the user 2958. For example, when a job profile isloaded to represent a state in the path topology, various feedbackwidgets may be loaded; for example, a database table may contain variousattributes that are associated with a given state and/or job and alsoare associated with various user interface templates and/or widgets,which may be loaded by the APB. Once the feedback widgets are loaded foran associated job profile 2958, the APB may then monitor for interactionwith the feedback widgets 2960 (for example, as already discussed inFIG. 28). If no interaction is detected, the APB may continue to monitor2960 until the interface is terminated. If the user does interact withthe feedback widget(s) 2962, the values obtained from that interactionare stored in the APB database record associated with job profile 2964.The APB then determines if the user supplying the feedback has authorityto do so 2966; for example, if the user is not currently employed forthe selected type of job for which the feedback is proffered, and it wasnot previously an experience of the user, then such feedback may bedeprecated (e.g., given low weight, may not be stored, and/or the like).The APB may then determine the weight to be given based on the userauthority; for example, feedback from users whose most currentemployment is the same state for which they are offering feedback willhave higher weight than for feedback from users who had such experiencefurther back in their career; which in turn may have higher weight thanfeedback from a user who has no such experience and/or less relatedequivalent job state experience. In one embodiment, users with currentexperience in the equivalent state receive a weight of 1.0, while usershaving experience in the past receive a weight of 0.5, while usershaving no experience receive a weight of 0.1. Optionally, the APB maythen collect behavioral (e.g., usage frequency), demographic,psychographic, and/or the like information and store it as associatedattribute information 2970. For example, a user's profile may includetheir geographic region, and as such, feedback from users in one regionmay be analyzed distinctly from users in differing regions. The APB maythen store the feedback as records entered as attributes in a APBdatabase, which is associated with job information, state information,and/or the like and the APB may continue to cycle for any selected jobprofiles 2972, 2956.

In one embodiment, as the APB continues to track feedback informationrelating to job profiles 2954, it may periodically query its databasefor the feedback for purposes of analysis 2974. In one embodiment, acron job may be executed at specified periods to perform an SQL selectfor unanalyzed feedback from the APB database. The APB may determine ifany filter (e.g., demographic and/or other selection criteria) should beused for the analysis 2976. If so, such modifying selectors may besupplied as part of the query 2978. The returned feedback records areanalyzed 2980, in one embodiment, using statistical frequency. Forexample, if a substantial number of seeker provide low confidenceratings for search results of a particular state, e.g., SystemsProgrammer, resulting from a particular query term, e.g., programmer;then this information may be used to demote state structureassociations. In one example embodiment, each demotion may act tosubtract the occurrence of a state traversal link. The APB may thenallow a user to make additional subset selections 2984, which result infurther results narrowing through more queries 2974. Otherwise the APBmay determine if there is any indication to terminate 2986 and end, orotherwise continue on tracking user interactions with the job profile2954.

Benchmarking

FIG. 31 is of a logic flow diagram illustrating benchmarking embodimentsfor the APB. In one embodiment, the APB allows one to select criteriasuch as a user identifier as a target of benchmarking 3105. In oneembodiment, an APB interface may allow a logged in user to make abenchmark selection for any given piece of advancement experienceinformation by right clicking on a desired target; e.g., right clickingon the target state in a path 2543 of FIG. 25, which brings up an optionto perform benchmark analytics on the user and/or the target state. TheAPB may determine the seeker's current job state identifier 3110. Forexample, if the user makes a specific selection of a state representingadvancement experience information in a state graph topology as in 2543of FIG. 25, then the selected item's state ID will be the seeker'scurrent job state 3110. Such a scenario allows the seeker to perform“what if” analytics on any state in the topography, and on any state intheir experience path, or on any state in a career path. However, if nospecific state is selected, in another embodiment, the seeker's mostcurrent experience information, e.g., there most current employmentposition, may be used as the current job state 3110. The APB may thenlook up the seeker's current experience characteristics. In oneembodiment, a seeker may populate their user profile withcharacteristics of their advancement experience information. Forexample, for any given historical employment position, the seeker mayalso supply characteristic attributes regarding that employmentpositions, attributes that may include salary, geographic location,hours of work (e.g., total number of hours worked per year), vacationduration, benefits, and/or the like. With this information stored in theseeker's user profile, it may determine these values by finding theseeker's profile record by the seeker ID and retrieving the relevantattributes stored therein 3115. In an alternative embodiment, the APBmay make an inquiry to companies connected to the APB that the seekerclaims as elements of past advancement experiences, and the APB mayquery those employer records through a gateway to automatically retrievethe attribute information. Upon obtaining the characteristics of theseeker's current job 3115, the APB determines if path dependant analysisis desired, and if so, the APB will determine the seeker's previous jobstates and corresponding characteristics, iterating, 3122 until all suchinformation for each, e.g., career, experience is determined 3122. Or ifindependent path analysis is required or upon determining the pathdependant information 3122, then the APB may provide a list of relevantjob characteristics to the seeker for selection 3125. For example, aftermaking a selection of a single or multiple states in the state pathgraph topology and selecting “benchmark” as in 2543 of FIG. 25, the APBmay present the user with a list of available benchmarking attributes ina dialogue box as shown in 3250 of FIG. 32, which provides check boxselectors for one or more characteristics. The APB will then determineif the seeker selected one or more criteria for benchmarking purposes3127. If there is more than one selection 3127, then APB may determineif default weights for each of the criteria are to be used 3130. In oneembodiment, an attribute database table may maintain weights for each ofthe attributes. In an alternative embodiment, each of the selectedweights may be equal. If there is indication that the seeker does notwant to use default weights, the seeker may then provide weights 3135.In one embodiment, weights may be entered into a text box 3252, bymaking selections on a slider 3254, by making selections of weights froma pop-up menu 3256 of FIG. 32, and/or the like interface widget. Oncethe weights are obtained, they may be stored 3137 in a user profile asweights associated with the current job state, and/or otherwisepersisted for use by the APB 3137. Once weights are established 3130,3137, or no weights are used 3127, the APB may then query the CSE forthe selected state and attribute data table for associated attributes toprovide statistical surveys and benchmarking information 3138. Forexample, in one embodiment, the CSE may be queried with the user'scurrent job state for all other instances of that job state encounteredby other job seekers that were used to make up the advancement statestructure in the CSE, and each of those returned query states having astate_ID may be used as a basis to query an attribute table with suchstate_IDs for associated attribute information. The returned attributesearch selection results may be averaged, aggregated, and or run throughstatistical packages such as SAS (e.g., via API, pipe, messages, and/orthe like) to generate covariance and other statistical informationand/or plots. Such analyses may include statistical processing andevaluation (e.g., the calculation of means, medians, variances, standarddeviations, covariances, correlations, and/or the like). For example,the salary attributes from the select may be aggregated, summed andaveraged and as such provide a benchmark against the user's current jobstate. In an alternative embodiment, the user may provide filterinformation which may be supplied as query select attribute as well3115; for example, a user may wish to have the average salary for ageographic region. Upon obtaining the statistical attribute information3138, the APB may use the returned information to generate visualizationplots for display 3140. Thereafter the seeker may make changes to theweights and job state selections so as to vary the benchmark results3145, and if so, benchmarking may recurse 3120. In another embodiment,gap analysis may be performed 3138 and displayed 3140.

FIG. 32 is of a block diagram illustrating benchmarking interfaceembodiments for the APB. In one embodiment, for the specification ofattributes to be benched marked 3250, the APB may use an interfaceselection mechanism that allows a seeker to specify whichcharacteristics are to be benchmarked, locations, settings and weightsfor each selected attribute 3252, 3254, 3256, as has already beendiscussed. Upon selecting an attribute, e.g., salary, the APB maygenerate a curve showing where a curve representing a plot of otherstates with attribute values. For example, for salaries, a curverepresenting the distribution of salaries for states in the statestructure may be plotted with 3260, 3261 and the curve allows a user toplot an orange dot along a curve 3263 that auto-populates a text box3266 with format validated salary information by querying the attributedatabase. The user may then confirm that the value is correct, if thesalary is monthly, annual, and if commissions are included 3267. In oneembodiment, orange dots will show if there are at least 4 or 5 otherrelevant user records that exist with the specified attribute for thatstate. Similar benchmark plots may be achieved for salary and vacationtime 3270. Also, plots employing attribute weights may also be employed3280. In yet another embodiment, multi-dimensional 3290 plots may showstate attribute distributions across, e.g., vacation days 3296, salary3294, and likelihood 3298 distributions.

Cloning

FIG. 33 is of a mixed logic and block diagram illustrating path cloningembodiments for the APB. In one embodiment, the APB may determine toperiodically engage and process unanalyzed seeker profiles 3301. In oneembodiment, a batch process may be engaged by cron at specifiedintervals. If the interval quantum has not occurred, then the APB willcontinue to wait until the occurrence of the period 3301. Uponoccurrence and/or passage of the quantum 3301, the APB may queue allanalyzed profiles 3303. In one embodiment, this may be achieved byquerying the APB database for seeker's profiles without stored, e.g.,career, state paths representing the seeker's experience information.Upon returning the query results, for each such seeker profile, the APBwill iterate 3305. The APB will identify a, e.g., career, state pathrepresenting the seeker's experience information. As has already beendiscussed in earlier figures, the APB may map each, e.g., career,experience item (e.g., job experience) to a state and by querying thestate structure for a states matching each of the seeker's experienceitems (See FIGS. 15, 16, et al.). As such, upon identifying all theassociated states for each seeker experience item, the APB may build andthereby identify a state path for each seeker's experience information3308. The APB may then store this state path in the seeker profile 3319and set a flag in the profile indicating that the path has beenconstructed and date when that path was constructed. The APB maydetermine if there are more profiles in the queue and if it mustcontinue to iterate for other unanalyzed profiles 3312. If there aremore unanalyzed profile, the APB may continue to generate career pathsfor each 3305, otherwise the APB may continue on and wait to repeat theprocess upon the occurrence of the next specified quantum 3301. In thisembodiment, the APB continues to update state paths for every seeker'sexperience information.

In so doing, all seeker's paths become available for analysis. In oneembodiment, the APB provides an interface and a mechanism to identifyand “clone” a specified seeker, by finding another seeker with identicaland/or similar, e.g., career, state path. In one embodiment, the APBprovides a web interface 3377 where an interested party, e.g., anemployer, may provide the experience information of a source candidateto be cloned. The APB may allow the interested party to enter a searchfor a specific candidate 3320, where results to the search terms may belisted 3322 for selection by the interested party 3322. In oneembodiment the interested party enters terms into a search field 3320,engages a “find” button 3324, and the APB will query for matchingcandidates and list the closest matching results 3322 from which theinterested party may make selections 3322. In another embodiment, theinterested party may search their file system for a source candidatesexperience information (e.g., a resume) or provide such 3330. In oneembodiment, the APB allows the interested party to search theircomputer's file structure and list files for selections by engaging a“submit resume” button 3326, which will bring up the a file browserwindow through which the interested party may specify (e.g., drag-n-dropa resume document 3330) the source experience information. After theinterested party selects what experience information it wishes to be thesource 3328, the interested party may ask the APB to “make a clone,”i.e., to identify another seeker having similar background and/orexperiences.

As such, the APB may analyze the source's experience information andgenerate an experience path as has already been discussed. In oneembodiment, upon obtaining a source experience path 3314, the APB maydisplay the source's path 3392. The APB may then query its database forother seekers having the same experience information 3316. In oneembodiment this may be achieved by using the source's state_IDs for eachentry comprising its experience state path as a basis to select from itsdatabase. Then for the query results, for each candidate having all thematched states, the APB may further filter and rank the results 3317. Itshould be noted that an interested party may also apply attributes as afilter 3317, 3337; for example, by searching for other candidates withthe same career path, but that have a set salary expectation (e.g., lessthan $50,000); one embodiment, the filter attributes may be provided ina popup menu 3337, a text field, a slider widget, and/or the likemechanism. In one embodiment, the APB may provide higher ranks formatches from the same regions, having experiences in the same order, andhaving other associated attributes (e.g., salary) that are most similarto the source seeker. In one embodiment, the APB may provide a pop-upmenu interface to select the manner in which results are ranked 3347. Inone embodiment, the rank clones 3346 may be displayed showing theirmatching paths 3393, 3394, 3395. The APB may rank the results by listingthe paths that have the greatest number of states in common with thesource more prominently than those having less matching states. The APBmay then display the next closet “clone” or list of clones 3318, 3393,3394, 3395 for review by the interested party. In one embodiment, theinterested party may send offers, propositions, solicitations, and/orotherwise provide a clone with information about advancementopportunities. In one embodiment, a user may make checkbox selections3396 of the desired clones and request to see the resumes of thoseselected clones 3344, upon which the APB will provide access to thoseclones. In another embodiment, an offer may be made by selecting thebutton 3344. In this way, interested parties may identify qualifiedindividuals for advancement. It should be noted that a seeker'sexperience information may also include a state experience pathcomprising their education history. As such, in one embodiment, the APBmay clone not only a seeker's, e.g., career, path experience, but alsotheir education path experience.

Advancement Taxonomy

FIG. 34 is of a mixed block and data flow diagram illustratingadvancement taxonomy embodiments for the APB. In one embodiment, the APBmay act as a “rosetta stone” as between a state structure (e.g., astates table) 3419 f, 3410, attribute information (e.g., an attributestable) 3419 j, and an experience structure 3419 h. In one embodiment,the APB may take process experience structure records 3401 from theexperiences table 3419 h and map them to the appropriate state.Similarly, in one optional embodiment, the APB may take attributerecords 3403 b from the attributes table 3419 j and map them to theappropriate state. As a consequence, the state structure and its states3419 f will be associated with both the experience structure and itsOccupational Classification codes (hereinafter “OC_code”) and withattribute information and its attribute_ID. An example of an OC_code isan Occupational Information Network (ONet) Standard OccupationalClassification (SOC) code. Similarly, once an association is made foreither experience structure 3422 a or attribute information 3423 a intoa state 3419 f, 3411, the APB may push (i.e., cause a database write ofa value in a record field) a unique state_ID into the experience table3433 and attribute table 3434. As such, with an experience table havinga state_ID, it may use that state_ID to access the appropriate state ina state structure, and in turn, look up an associated attributes tableentry; and vice versa for the attributes table being able to map to theexperience structure. In another embodiment, the APB may push its ownstate_ID 3433 and an attributes ID 3423 b into the experience table, andits own state_ID 3434 and an OC_code 3422 b into the attributesinformation 3419 i so as to minimize database traversal. In anotherembodiment, simultaneous writes 3422, 3423, 3433, 3434 may take place.

In one embodiment, the APB 3408 may use the experience table's title,job description, skills, category, keyword and other field values asbasis to discern and map to a matching state in the state structure, ashas already been discussed in FIG. 15 et seq. In one embodiment, the CSE3408 may similarly use values stored in the attributes table 3419 j.However, in an alternative embodiment, attribute information 3419 h andexperience information 3419 i may be related by being assigned byadministrators who will fine tune said associations.

In another embodiment, attributes 3419 i that are related to experienceinformation 3419 h assume a relationship that is discerned as betweenthe experience information 3419 h and a state 3419 f. For example, acareer system, such as Monster.com, may track attributes for various joblistings that may be stored in a job listing table 3619 l of FIG. 36.Such job listings often have numerous attributes and many otherattributes may be discerned through statistical analysis of seekers thatinteracted with job listings. These job listings often have an OC_code,and as such may already be related to experiences 3619 h of FIG. 36 inan experience structure 3619 g of FIG. 36. As has already beendiscussed, the APB may associate unmapped experiences 3401, 3419 h tostates 3410, 3419 f, and when so doing, it may relate attributeinformation 3419 i that has already been associated to the unmappedexperience 3419 h to states in the same process. In another embodiment,structured resume information, i.e., experiences 3419 h may be mapped toan OC_codes as described in patent application Ser. No. 11/615,765 filedDec. 22, 2006, entitled “APPARATUSES, METHODS AND SYSTEMS FOR ANINTERACTIVE EMPLOYMENT SEARCH PLATFORM,” and Ser. No. 11/615,768 filedDec. 22, 2006, entitled “A METHOD FOR INTERACTIVE EMPLOYMENT SEARCHINGAND SKILLS SPECIFICATION,”; the entire contents of both applications ishereby expressly incorporated by reference.

In one embodiment, the entities in FIG. 34 correspond to APBController's database component tables 3619, in that they correspond toFIG. 36's last two digit reference numbers, i.e., 3419 h corresponds totable 3619 h of FIG. 34.

FIG. 35 is of a block diagram illustrating advancement taxonomyrelationships and embodiments for the APB. Further to the base taxonomyintroduced in FIG. 34, the APB may similarly forge relationships ofvarious information bases through the state structure as shown in FIG.35. Similarly, all of the APB Controller's database component'sinterrelated database tables 3619 of FIG. 36 may be brought intoassociation with the state structure as was done with the attributeinformation in FIG. 34. FIG. 35 shows one embodiment of some of the APBController's database component's tables cast into numerous types ofconnections and relationships (one to one, 1:1; one to many, 1→M, manyto many, M→M; many to one, M→1) as between various structures andsupporting database tables. It should be noted that in one embodiment,the APB database tables are all interconnected in a manner where everytable is related to every other table.

In one embodiment, the entities in FIG. 35 correspond to APBController's database component tables 3619, in that they correspond toFIG. 36's last two digit reference numbers, i.e., 3519 m corresponds totable 3619 m of FIG. 34.

APB Controller

FIG. 36 illustrates inventive aspects of a APB controller 3601 in ablock diagram. In this embodiment, the APB controller 3601 may serve toaggregate, process, store, search, serve, identify, instruct, generate,match, and/or facilitate interactions with a computer throughadvancement progression technologies, and/or other related data.

Typically, users, which may be people and/or other systems, may engageinformation technology systems (e.g., computers) to facilitateinformation processing. In turn, computers employ processors to processinformation; such processors 3603 may be referred to as centralprocessing units (CPU). One form of processor is referred to as amicroprocessor. CPUs use communicative circuits to pass binary encodedsignals acting as instructions to enable various operations. Theseinstructions may be operational and/or data instructions containingand/or referencing other instructions and data in various processoraccessible and operable areas of memory 3629 (e.g., registers, cachememory, random access memory, etc.). Such communicative instructions maybe stored and/or transmitted in batches (e.g., batches of instructions)as programs and/or data components to facilitate desired operations.These stored instruction codes, e.g., programs, may engage the CPUcircuit components and other motherboard and/or system components toperform desired operations. One type of program is a computer operatingsystem, which, may be executed by CPU on a computer; the operatingsystem enables and facilitates users to access and operate computerinformation technology and resources. Some resources that may employedin information technology systems include: input and output mechanismsthrough which data may pass into and out of a computer; memory storageinto which data may be saved; and processors by which information may beprocessed. These information technology systems may be used to collectdata for later retrieval, analysis, and manipulation, which may befacilitated through a database program. These information technologysystems provide interfaces that allow users to access and operatevarious system components.

In one embodiment, the APB controller 3601 may be connected to and/orcommunicate with entities such as, but not limited to: one or more usersfrom user input devices 3611; peripheral devices 3612; an optionalcryptographic processor device 3628; and/or a communications network3613.

Networks are commonly thought to comprise the interconnection andinteroperation of clients, servers, and intermediary nodes in a graphtopology. It should be noted that the term “server” as used throughoutthis application refers generally to a computer, other device, program,or combination thereof that processes and responds to the requests ofremote users across a communications network. Servers serve theirinformation to requesting “clients.” The term “client” as used hereinrefers generally to a computer, program, other device, user and/orcombination thereof that is capable of processing and making requestsand obtaining and processing any responses from servers across acommunications network. A computer, other device, program, orcombination thereof that facilitates, processes information andrequests, and/or furthers the passage of information from a source userto a destination user is commonly referred to as a “node.” Networks aregenerally thought to facilitate the transfer of information from sourcepoints to destinations. A node specifically tasked with furthering thepassage of information from a source to a destination is commonly calleda “router.” There are many forms of networks such as Local Area Networks(LANs), Pico networks, Wide Area Networks (WANs), Wireless Networks(WLANs), etc. For example, the Internet is generally accepted as beingan interconnection of a multitude of networks whereby remote clients andservers may access and interoperate with one another.

The APB controller 3601 may be based on computer systems that maycomprise, but are not limited to, components such as: a computersystemization 3602 connected to memory 3629.

Computer Systemization

A computer systemization 3602 may comprise a clock 3630, centralprocessing unit (“CPU(s)” and/or “processor(s)” (these terms are usedinterchangeable throughout the disclosure unless noted to the contrary))3603, a memory 3629 (e.g., a read only memory (ROM) 3606, a randomaccess memory (RAM) 3605, etc.), and/or an interface bus 3607, and mostfrequently, although not necessarily, are all interconnected and/orcommunicating through a system bus 3604 on one or more (mother)board(s)3602 having conductive and/or otherwise transportive circuit pathwaysthrough which instructions (e.g., binary encoded signals) may travel toeffect communications, operations, storage, etc. Optionally, thecomputer systemization may be connected to an internal power source3686. Optionally, a cryptographic processor 3626 may be connected to thesystem bus. The system clock typically has a crystal oscillator andgenerates a base signal through the computer systemization's circuitpathways. The clock is typically coupled to the system bus and variousclock multipliers that will increase or decrease the base operatingfrequency for other components interconnected in the computersystemization. The clock and various components in a computersystemization drive signals embodying information throughout the system.Such transmission and reception of instructions embodying informationthroughout a computer systemization may be commonly referred to ascommunications. These communicative instructions may further betransmitted, received, and the cause of return and/or replycommunications beyond the instant computer systemization to:communications networks, input devices, other computer systemizations,peripheral devices, and/or the like. Of course, any of the abovecomponents may be connected directly to one another, connected to theCPU, and/or organized in numerous variations employed as exemplified byvarious computer systems.

The CPU comprises at least one high-speed data processor adequate toexecute program components for executing user and/or system-generatedrequests. Often, the processors themselves will incorporate variousspecialized processing units, such as, but not limited to: integratedsystem (bus) controllers, memory management control units, floatingpoint units, and even specialized processing sub-units like graphicsprocessing units, digital signal processing units, and/or the like.Additionally, processors may include internal fast access addressablememory, and be capable of mapping and addressing memory 529 beyond theprocessor itself; internal memory may include, but is not limited to:fast registers, various levels of cache memory (e.g., level 1, 2, 3,etc.), RAM, etc. The processor may access this memory through the use ofa memory address space that is accessible via instruction address, whichthe processor can construct and decode allowing it to access a circuitpath to a specific memory address space having a memory state. The CPUmay be a microprocessor such as: AMD's Athlon, Duron and/or Opteron;ARM's application, embedded and secure processors; IBM and/or Motorola'sDragonBall and PowerPC; IBM's and Sony's Cell processor; Intel'sCeleron, Core (2) Duo, Itanium, Pentium, Xeon, and/or XScale; and/or thelike processor(s). The CPU interacts with memory through instructionpassing through conductive and/or transportive conduits (e.g., (printed)electronic and/or optic circuits) to execute stored instructions (i.e.,program code) according to conventional data processing techniques. Suchinstruction passing facilitates communication within the APB controllerand beyond through various interfaces. Should processing requirementsdictate a greater amount speed and/or capacity, distributed processors(e.g., Distributed APB), mainframe, multi-core, parallel, and/orsuper-computer architectures may similarly be employed. Alternatively,should deployment requirements dictate greater portability, smallerPersonal Digital Assistants (PDAs) may be employed.

Depending on the particular implementation, features of the APB may beachieved by implementing a microcontroller such as CAST's R8051XC2microcontroller; Intel's MCS 51 (i.e., 8051 microcontroller); and/or thelike. Also, to implement certain features of the APB, some featureimplementations may rely on embedded components, such as:Application-Specific Integrated Circuit (“ASIC”), Digital SignalProcessing (“DSP”), Field Programmable Gate Array (“FPGA”), and/or thelike embedded technology. For example, any of the APB componentcollection (distributed or otherwise) and/or features may be implementedvia the microprocessor and/or via embedded components; e.g., via ASIC,coprocessor, DSP, FPGA, and/or the like. Alternately, someimplementations of the APB may be implemented with embedded componentsthat are configured and used to achieve a variety of features or signalprocessing.

Depending on the particular implementation, the embedded components mayinclude software solutions, hardware solutions, and/or some combinationof both hardware/software solutions. For example, APB features discussedherein may be achieved through implementing FPGAs, which are asemiconductor devices containing programmable logic components called“logic blocks”, and programmable interconnects, such as the highperformance FPGA Virtex series and/or the low cost Spartan seriesmanufactured by Xilinx. Logic blocks and interconnects can be programmedby the customer or designer, after the FPGA is manufactured, toimplement any of the APB features. A hierarchy of programmableinterconnects allow logic blocks to be interconnected as needed by theAPB system designer/administrator, somewhat like a one-chip programmablebreadboard. An FPGA's logic blocks can be programmed to perform thefunction of basic logic gates such as AND, and XOR, or more complexcombinational functions such as decoders or simple mathematicalfunctions. In most FPGAs, the logic blocks also include memory elements,which may be simple flip-flops or more complete blocks of memory. Insome circumstances, the APB may be developed on regular FPGAs and thenmigrated into a fixed version that more resembles ASIC implementations.Alternate or coordinating implementations may migrate APB controllerfeatures to a final ASIC instead of or in addition to FPGAs. Dependingon the implementation all of the aforementioned embedded components andmicroprocessors may be considered the “CPU” and/or “processor” for theAPB.

Power Source

The power source 3686 may be of any standard form for powering smallelectronic circuit board devices such as the following power cells:alkaline, lithium hydride, lithium ion, lithium polymer, nickel cadmium,solar cells, and/or the like. Other types of AC or DC power sources maybe used as well. In the case of solar cells, in one embodiment, the caseprovides an aperture through which the solar cell may cAPBure photonicenergy. The power cell 3686 is connected to at least one of theinterconnected subsequent components of the APB thereby providing anelectric current to all subsequent components. In one example, the powersource 3686 is connected to the system bus component 3604. In analternative embodiment, an outside power source 3686 is provided througha connection across the I/O 3608 interface. For example, a USB and/orIEEE 1394 connection carries both data and power across the connectionand is therefore a suitable source of power.

Interface Adapters

Interface bus(ses) 3607 may accept, connect, and/or communicate to anumber of interface adAPBers, conventionally although not necessarily inthe form of adapter cards, such as but not limited to: input outputinterfaces (I/O) 3608, storage interfaces 3609, network interfaces 3610,and/or the like. Optionally, cryptographic processor interfaces 3627similarly may be connected to the interface bus. The interface busprovides for the communications of interface adapters with one anotheras well as with other components of the computer systemization.Interface adapters are adapted for a compatible interface bus. Interfaceadapters conventionally connect to the interface bus via a slotarchitecture. Conventional slot architectures may be employed, such as,but not limited to: Accelerated Graphics Port (AGP), Card Bus,(Extended) Industry Standard Architecture ((E)ISA), Micro ChannelArchitecture (MCA), NuBus, Peripheral Component Interconnect (Extended)(PCI(X)), PCI Express, Personal Computer Memory Card InternationalAssociation (PCMCIA), and/or the like.

Storage interfaces 3609 may accept, communicate, and/or connect to anumber of storage devices such as, but not limited to: storage devices3614, removable disc devices, and/or the like. Storage interfaces mayemploy connection protocols such as, but not limited to: (Ultra)(Serial) Advanced Technology Attachment (Packet Interface) ((Ultra)(Serial) ATA(PI)), (Enhanced) Integrated Drive Electronics ((E)IDE),Institute of Electrical and Electronics Engineers (IEEE) 1394, fiberchannel, Small Computer Systems Interface (SCSI), Universal Serial Bus(USB), and/or the like.

Network interfaces 3610 may accept, communicate, and/or connect to acommunications network 3613. Through a communications network 3613, theAPB controller is accessible through remote clients 3633 b (e.g.,computers with web browsers) by users 3633 a. Network interfaces mayemploy connection protocols such as, but not limited to: direct connect,Ethernet (thick, thin, twisted pair 10/100/1000 Base T, and/or thelike), Token Ring, wireless connection such as IEEE 802.11a-x, and/orthe like. Should processing requirements dictate a greater amount speedand/or capacity, distributed network controllers (e.g., DistributedAPB), architectures may similarly be employed to pool, load balance,and/or otherwise increase the communicative bandwidth required by theAPB controller. A communications network may be any one and/or thecombination of the following: a direct interconnection; the Internet; aLocal Area Network (LAN); a Metropolitan Area Network (MAN); anOperating Missions as Nodes on the Internet (OMNI); a secured customconnection; a Wide Area Network (WAN); a wireless network (e.g.,employing protocols such as, but not limited to a Wireless ApplicationProtocol (WAP), I-mode, and/or the like); and/or the like. A networkinterface may be regarded as a specialized form of an input outputinterface. Further, multiple network interfaces 3610 may be used toengage with various communications network types 3613. For example,multiple network interfaces may be employed to allow for thecommunication over broadcast, multicast, and/or unicast networks.

Input Output interfaces (I/O) 3608 may accept, communicate, and/orconnect to user input devices 3611, peripheral devices 3612,cryptographic processor devices 3628, and/or the like. I/O may employconnection protocols such as, but not limited to: audio: analog,digital, monaural, RCA, stereo, and/or the like; data: Apple Desktop Bus(ADB), IEEE 1394a-b, serial, universal serial bus (USB); infrared;joystick; keyboard; midi; optical; PC AT; PS/2; parallel; radio; videointerface: Apple Desktop Connector (ADC), BNC, coaxial, component,composite, digital, Digital Visual Interface (DVI), high-definitionmultimedia interface (HDMI), RCA, RF antennae, S-Video, VGA, and/or thelike; wireless: 802.11a/b/g/n/x, Bluetooth, code division multipleaccess (CDMA), global system for mobile communications (GSM), WiMax,etc.; and/or the like. One typical output device may include a videodisplay, which typically comprises a Cathode Ray Tube (CRT) or LiquidCrystal Display (LCD) based monitor with an interface (e.g., DVIcircuitry and cable) that accepts signals from a video interface, may beused. The video interface composites information generated by a computersystemization and generates video signals based on the compositedinformation in a video memory frame. Another output device is atelevision set, which accepts signals from a video interface. Typically,the video interface provides the composited video information through avideo connection interface that accepts a video display interface (e.g.,an RCA composite video connector accepting an RCA composite video cable;a DVI connector accepting a DVI display cable, etc.).

User input devices 3611 may be card readers, dongles, finger printreaders, gloves, graphics tablets, joysticks, keyboards, mouse (mice),remote controls, retina readers, trackballs, trackpads, and/or the like.

Peripheral devices 3612 may be connected and/or communicate to I/Oand/or other facilities of the like such as network interfaces, storageinterfaces, and/or the like. Peripheral devices may be audio devices,cameras, dongles (e.g., for copy protection, ensuring securetransactions with a digital signature, and/or the like), externalprocessors (for added functionality), goggles, microphones, monitors,network interfaces, printers, scanners, storage devices, video devices,video sources, visors, and/or the like.

It should be noted that although user input devices and peripheraldevices may be employed, the APB controller may be embodied as anembedded, dedicated, and/or monitor-less (i.e., headless) device,wherein access would be provided over a network interface connection.

Cryptographic units such as, but not limited to, microcontrollers,processors 3626, interfaces 3627, and/or devices 3628 may be attached,and/or communicate with the APB controller. A MC68HC16 microcontroller,manufactured by Motorola Inc., may be used for and/or withincryptographic units. The MC68HC16 microcontroller utilizes a 16-bitmultiply-and-accumulate instruction in the 16 MHz configuration andrequires less than one second to perform a 512-bit RSA private keyoperation. Cryptographic units support the authentication ofcommunications from interacting agents, as well as allowing foranonymous transactions. Cryptographic units may also be configured aspart of CPU. Equivalent microcontrollers and/or processors may also beused. Other commercially available specialized cryptographic processorsinclude: the Broadcom's CryptoNetX and other Security Processors;nCipher's nShield, SafeNet's Luna PCI (e.g., 7100) series; SemaphoreCommunications' 40 MHz Roadrunner 184; Sun's Cryptographic Accelerators(e.g., Accelerator 6000 PCIe Board, Accelerator 500 Daughtercard); ViaNano Processor (e.g., L2100, L2200, U2400) line, which is capable ofperforming 500+MB/s of cryptographic instructions; VLSI Technology's 33MHz 6868; and/or the like.

Memory

Generally, any mechanization and/or embodiment allowing a processor toaffect the storage and/or retrieval of information is regarded as memory3629. However, memory is a fungible technology and resource, thus, anynumber of memory embodiments may be employed in lieu of or in concertwith one another. It is to be understood that the APB controller and/ora computer systemization may employ various forms of memory 3629. Forexample, a computer systemization may be configured wherein thefunctionality of on-chip CPU memory (e.g., registers), RAM, ROM, and anyother storage devices are provided by a paper punch tape or paper punchcard mechanism; of course such an embodiment would result in anextremely slow rate of operation. In a typical configuration, memory3629 will include ROM 3606, RAM 3605, and a storage device 3614. Astorage device 3614 may be any conventional computer system storage.Storage devices may include a drum; a (fixed and/or removable) magneticdisk drive; a magneto-optical drive; an optical drive (i.e., Blueray, CDROM/RAM/Recordable (R)/ReWritable (RW), DVD R/RW, HD DVD R/RW etc.); anarray of devices (e.g., Redundant Array of Independent Disks (RAID));solid state memory devices (USB memory, solid state drives (SSD), etc.);and/or other devices of the like. Thus, a computer systemizationgenerally requires and makes use of memory.

Component Collection

The memory 3629 may contain a collection of program and/or databasecomponents and/or data such as, but not limited to: operating systemcomponent(s) 3615 (operating system); information server component(s)3616 (information server); user interface component(s) 3617 (userinterface); Web browser component(s) 3618 (Web browser); database(s)3619; mail server component(s) 3621; mail client component(s) 3622;cryptographic server component(s) 3620 (cryptographic server); CSEcomponent(s) 3655; the APB component(s) 3635; and/or the like (i.e.,collectively a component collection). These components may be stored andaccessed from the storage devices and/or from storage devices accessiblethrough an interface bus. Although non-conventional program componentssuch as those in the component collection, typically, are stored in alocal storage device 3614, they may also be loaded and/or stored inmemory such as: peripheral devices, RAM, remote storage facilitiesthrough a communications network, ROM, various forms of memory, and/orthe like.

Operating System

The operating system component 3615 is an executable program componentfacilitating the operation of the APB controller. Typically, theoperating system facilitates access of I/O, network interfaces,peripheral devices, storage devices, and/or the like. The operatingsystem may be a highly fault tolerant, scalable, and secure system suchas: Apple Macintosh OS X (Server); AT&T Plan 9; Be OS; Unix andUnix-like system distributions (such as AT&T's UNIX; Berkley SoftwareDistribution (BSD) variations such as FreeBSD, NetBSD, OpenBSD, and/orthe like; Linux distributions such as Red Hat, Ubuntu, and/or the like);and/or the like operating systems. However, more limited and/or lesssecure operating systems also may be employed such as Apple MacintoshOS, IBM OS/2, Microsoft DOS, Microsoft Windows2000/2003/3.1/95/98/CE/Millenium/NTNista/XP (Server), Palm OS, and/orthe like. An operating system may communicate to and/or with othercomponents in a component collection, including itself, and/or the like.Most frequently, the operating system communicates with other programcomponents, user interfaces, and/or the like. For example, the operatingsystem may contain, communicate, generate, obtain, and/or provideprogram component, system, user, and/or data communications, requests,and/or responses. The operating system, once executed by the CPU, mayenable the interaction with communications networks, data, I/O,peripheral devices, program components, memory, user input devices,and/or the like. The operating system may provide communicationsprotocols that allow the APB controller to communicate with otherentities through a communications network 3613. Various communicationprotocols may be used by the APB controller as a subcarrier transportmechanism for interaction, such as, but not limited to: multicast,TCP/IP, UDP, unicast, and/or the like.

Information Server

An information server component 3616 is a stored program component thatis executed by a CPU. The information server may be a conventionalInternet information server such as, but not limited to Apache SoftwareFoundation's Apache, Microsoft's Internet Information Server, and/or thelike. The information server may allow for the execution of programcomponents through facilities such as Active Server Page (ASP), ActiveX,(ANSI) (Objective-) C (++), C# and/or .NET, Common Gateway Interface(CGI) scripts, dynamic (D) hypertext markup language (HTML), FLASH,Java, JavaScript, Practical Extraction Report Language (PERL), HypertextPre-Processor (PHP), pipes, Python, wireless application protocol (WAP),WebObjects, and/or the like. The information server may support securecommunications protocols such as, but not limited to, File TransferProtocol (FTP); HyperText Transfer Protocol (HTTP); Secure HypertextTransfer Protocol (HTTPS), Secure Socket Layer (SSL), messagingprotocols (e.g., America Online (AOL) Instant Messenger (AIM),Application Exchange (APEX), ICQ, Internet Relay Chat (IRC), MicrosoftNetwork (MSN) Messenger Service, Presence and Instant Messaging Protocol(PRIM), Internet Engineering Task Force's (IETF's) Session InitiationProtocol (SIP), SIP for Instant Messaging and Presence LeveragingExtensions (SIMPLE), open XML-based Extensible Messaging and PresenceProtocol (XMPP) (i.e., Jabber or Open Mobile Alliance's (OMA's) InstantMessaging and Presence Service (IMPS)), Yahoo! Instant MessengerService, and/or the like. The information server provides results in theform of Web pages to Web browsers, and allows for the manipulatedgeneration of the Web pages through interaction with other programcomponents. After a Domain Name System (DNS) resolution portion of anHTTP request is resolved to a particular information server, theinformation server resolves requests for information at specifiedlocations on the APB controller based on the remainder of the HTTPrequest. For example, a request such ashttp://123.124.125.126/myInformation.html might have the IP portion ofthe request “123.124.125.126” resolved by a DNS server to an informationserver at that IP address; that information server might in turn furtherparse the http request for the “/myInformation.html” portion of therequest and resolve it to a location in memory containing theinformation “myInformation.html.” Additionally, other informationserving protocols may be employed across various ports, e.g., FTPcommunications across port 21, and/or the like. An information servermay communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Mostfrequently, the information server communicates with the APB database3619, operating systems, other program components, user interfaces, Webbrowsers, and/or the like.

Access to the APB database may be achieved through a number of databasebridge mechanisms such as through scripting languages as enumeratedbelow (e.g., CGI) and through inter-application communication channelsas enumerated below (e.g., CORBA, WebObjects, etc.). Any data requeststhrough a Web browser are parsed through the bridge mechanism intoappropriate grammars as required by the APB. In one embodiment, theinformation server would provide a Web form accessible by a Web browser.Entries made into supplied fields in the Web form are tagged as havingbeen entered into the particular fields, and parsed as such. The enteredterms are then passed along with the field tags, which act to instructthe parser to generate queries directed to appropriate tables and/orfields. In one embodiment, the parser may generate queries in standardSQL by instantiating a search string with the proper join/selectcommands based on the tagged text entries, wherein the resulting commandis provided over the bridge mechanism to the APB as a query. Upongenerating query results from the query, the results are passed over thebridge mechanism, and may be parsed for formatting and generation of anew results Web page by the bridge mechanism. Such a new results Webpage is then provided to the information server, which may supply it tothe requesting Web browser.

Also, an information server may contain, communicate, generate, obtain,and/or provide program component, system, user, and/or datacommunications, requests, and/or responses.

User Interface

The function of computer interfaces in some respects is similar toautomobile operation interfaces. Automobile operation interface elementssuch as steering wheels, gearshifts, and speedometers facilitate theaccess, operation, and display of automobile resources, functionality,and status. Computer interaction interface elements such as check boxes,cursors, menus, scrollers, and windows (collectively and commonlyreferred to as widgets) similarly facilitate the access, operation, anddisplay of data and computer hardware and operating system resources,functionality, and status. Operation interfaces are commonly called userinterfaces. Graphical user interfaces (GUIs) such as the Apple MacintoshOperating System's Aqua, IBM's OS/2, Microsoft's Windows2000/2003/3.1/95/98/CE/Millenium/NT/XP/Vista/7 (i.e., Aero), Unix'sX-Windows (e.g., which may include additional Unix graphic interfacelibraries and layers such as K Desktop Environment (KDE), mythTV and GNUNetwork Object Model Environment (GNOME)), web interface libraries(e.g., ActiveX, AJAX, (D)HTML, FLASH, Java, JavaScript, etc. interfacelibraries such as, but not limited to, Dojo, jQuery(UI), MooTools,Prototype, script.aculo.us, SWFObject, Yahoo! User Interface, any ofwhich may be used and) provide a baseline and means of accessing anddisplaying information graphically to users.

A user interface component 3617 is a stored program component that isexecuted by a CPU. The user interface may be a conventional graphic userinterface as provided by, with, and/or atop operating systems and/oroperating environments such as already discussed. The user interface mayallow for the display, execution, interaction, manipulation, and/oroperation of program components and/or system facilities through textualand/or graphical facilities. The user interface provides a facilitythrough which users may affect, interact, and/or operate a computersystem. A user interface may communicate to and/or with other componentsin a component collection, including itself, and/or facilities of thelike. Most frequently, the user interface communicates with operatingsystems, other program components, and/or the like. The user interfacemay contain, communicate, generate, obtain, and/or provide programcomponent, system, user, and/or data communications, requests, and/orresponses.

Web Browser

A Web browser component 3618 is a stored program component that isexecuted by a CPU. The Web browser may be a conventional hypertextviewing application such as Microsoft Internet Explorer or NetscapeNavigator. Secure Web browsing may be supplied with bit (or greater)encryption by way of HTTPS, SSL, and/or the like. Web browsers allowingfor the execution of program components through facilities such asActiveX, AJAX, (D)HTML, FLASH, Java, JavaScript, web browser plug-inAPIs (e.g., FireFox, Safari Plug-in, and/or the like APIs), and/or thelike. Web browsers and like information access tools may be integratedinto PDAs, cellular telephones, and/or other mobile devices. A Webbrowser may communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Mostfrequently, the Web browser communicates with information servers,operating systems, integrated program components (e.g., plug-ins),and/or the like; e.g., it may contain, communicate, generate, obtain,and/or provide program component, system, user, and/or datacommunications, requests, and/or responses. Of course, in place of a Webbrowser and information server, a combined application may be developedto perform similar functions of both. The combined application wouldsimilarly affect the obtaining and the provision of information tousers, user agents, and/or the like from the APB enabled nodes. Thecombined application may be nugatory on systems employing standard Webbrowsers.

Mail Server

A mail server component 3621 is a stored program component that isexecuted by a CPU 3603. The mail server may be a conventional Internetmail server such as, but not limited to sendmail, Microsoft Exchange,and/or the like. The mail server may allow for the execution of programcomponents through facilities such as ASP, ActiveX, (ANSI) (Objective-)C (++), C# and/or .NET, CGI scripts, Java, JavaScript, PERL, PHP, pipes,Python, WebObjects, and/or the like. The mail server may supportcommunications protocols such as, but not limited to: Internet messageaccess protocol (IMAP), Messaging Application Programming Interface(MAPI)/Microsoft Exchange, post office protocol (POP3), simple mailtransfer protocol (SMTP), and/or the like. The mail server can route,forward, and process incoming and outgoing mail messages that have beensent, relayed and/or otherwise traversing through and/or to the APB.

Access to the APB mail may be achieved through a number of APIs offeredby the individual Web server components and/or the operating system.

Also, a mail server may contain, communicate, generate, obtain, and/orprovide program component, system, user, and/or data communications,requests, information, and/or responses.

Mail Client

A mail client component 3622 is a stored program component that isexecuted by a CPU 3603. The mail client may be a conventional mailviewing application such as Apple Mail, Microsoft Entourage, MicrosoftOutlook, Microsoft Outlook Express, Mozilla, Thunderbird, and/or thelike. Mail clients may support a number of transfer protocols, such as:IMAP, Microsoft Exchange, POP3, SMTP, and/or the like. A mail client maycommunicate to and/or with other components in a component collection,including itself, and/or facilities of the like. Most frequently, themail client communicates with mail servers, operating systems, othermail clients, and/or the like; e.g., it may contain, communicate,generate, obtain, and/or provide program component, system, user, and/ordata communications, requests, information, and/or responses. Generally,the mail client provides a facility to compose and transmit electronicmail messages.

Cryptographic Server

A cryptographic server component 3620 is a stored program component thatis executed by a CPU 3603, cryptographic processor 3626, cryptographicprocessor interface 3627, cryptographic processor device 3628, and/orthe like. Cryptographic processor interfaces will allow for expeditionof encryption and/or decryption requests by the cryptographic component;however, the cryptographic component, alternatively, may run on aconventional CPU. The cryptographic component allows for the encryptionand/or decryption of provided data. The cryptographic component allowsfor both symmetric and asymmetric (e.g., Pretty Good Protection (PGP))encryption and/or decryption. The cryptographic component may employcryptographic techniques such as, but not limited to: digitalcertificates (e.g., X.509 authentication framework), digital signatures,dual signatures, enveloping, password access protection, public keymanagement, and/or the like. The cryptographic component will facilitatenumerous (encryption and/or decryption) security protocols such as, butnot limited to: checksum, Data Encryption Standard (DES), EllipticalCurve Encryption (ECC), International Data Encryption Algorithm (IDEA),Message Digest 5 (MD5, which is a one way hash function), passwords,Rivest Cipher (RC5), Rijndael, RSA (which is an Internet encryption andauthentication system that uses an algorithm developed in 1977 by RonRivest, Adi Shamir, and Leonard Adleman), Secure Hash Algorithm (SHA),Secure Socket Layer (SSL), Secure Hypertext Transfer Protocol (HTTPS),and/or the like. Employing such encryption security protocols, the APBmay encrypt all incoming and/or outgoing communications and may serve asnode within a virtual private network (VPN) with a wider communicationsnetwork. The cryptographic component facilitates the process of“security authorization” whereby access to a resource is inhibited by asecurity protocol wherein the cryptographic component effects authorizedaccess to the secured resource. In addition, the cryptographic componentmay provide unique identifiers of content, e.g., employing and MD5 hashto obtain a unique signature for an digital audio file. A cryptographiccomponent may communicate to and/or with other components in a componentcollection, including itself, and/or facilities of the like. Thecryptographic component supports encryption schemes allowing for thesecure transmission of information across a communications network toenable the APB component to engage in secure transactions if so desired.The cryptographic component facilitates the secure accessing ofresources on the APB and facilitates the access of secured resources onremote systems; i.e., it may act as a client and/or server of securedresources. Most frequently, the cryptographic component communicateswith information servers, operating systems, other program components,and/or the like. The cryptographic component may contain, communicate,generate, obtain, and/or provide program component, system, user, and/ordata communications, requests, and/or responses.

The APB Database

The APB database component 3619 may be embodied in a database and itsstored data. The database is a stored program component, which isexecuted by the CPU; the stored program component portion configuringthe CPU to process the stored data. The database may be a conventional,fault tolerant, relational, scalable, secure database such as Oracle orSybase. Relational databases are an extension of a flat file. Relationaldatabases consist of a series of related tables. The tables areinterconnected via a key field. Use of the key field allows thecombination of the tables by indexing against the key field; i.e., thekey fields act as dimensional pivot points for combining informationfrom various tables. Relationships generally identify links maintainedbetween tables by matching primary keys. Primary keys represent fieldsthat uniquely identify the rows of a table in a relational database.More precisely, they uniquely identify rows of a table on the “one” sideof a one-to-many relationship.

Alternatively, the APB database may be implemented using variousstandard data-structures, such as an array, hash, (linked) list, struct,structured text file (e.g., XML), table, and/or the like. Suchdata-structures may be stored in memory and/or in (structured) files. Inanother alternative, an object-oriented database may be used, such asFrontier, ObjectStore, Poet, Zope, and/or the like. Object databases caninclude a number of object collections that are grouped and/or linkedtogether by common attributes; they may be related to other objectcollections by some common attributes. Object-oriented databases performsimilarly to relational databases with the exception that objects arenot just pieces of data but may have other types of functionalityencapsulated within a given object. If the APB database is implementedas a data-structure, the use of the APB database 3619 may be integratedinto another component such as the APB component 3635. Also, thedatabase may be implemented as a mix of data structures, objects, andrelational structures. Databases may be consolidated and/or distributedin countless variations through standard data processing techniques.Portions of databases, e.g., tables, may be exported and/or imported andthus decentralized and/or integrated.

In one embodiment, the database component 3619 includes several tables3619 a-m (wherein the first listed field in each table is the key fieldand all fields with an “ID” suffix are fields having unique values), asfollows:

A seeker_profiles table 3619 a may include fields such as, but notlimited to: user_ID, name, address, contact_info, preferences, friends,status, user_description, attributes, experience_info_ID, path_ID(s),attribute_ID(s), and/or the like.

A seeker_experience table (aka “experience” or “resume” table) 3619 bmay include fields such as, but not limited to: experience_info_ID,experience_item_ID(s), education_item_ID(s), resume_data, skills,awards, honors, languages, current_salary_prefrences, user_ID(s),path_ID(s), and/or the like.

A experience_item table 3619 c may include fields such as, but notlimited to: experience_item_ID, institution_ID, job_title,job_description, job_dates, job_salary, skills, awards, honors,satisfaction_ratings, state_ID, OC_code(s), attribute_ID(s), and/or thelike.

A education_item table 3619 d may include fields such as, but notlimited to: education_item_ID, institution_ID,education_degree_subject_matter, education_item_description,education_degree, education_item_dates, skills, awards, honors,satisfaction_ratings, state_ID, attribute_ID(s), and/or the like.

A state_structure table 3619 e may include fields such as, but notlimited to: state_structure_ID, state_structure_data, state_ID(s),and/or the like.

A states table 3619 f may include fields such as, but not limited to:state_ID, state_name, job_titles, topics, next_states_ID,previous_states_ID, state transition_probabilities, job_title_count,total_count, topic_count, transition_weights, OC_code(s),attribute_ID(s), user_ID(s), and/or the like.

A experience_structure table 3619 g may include fields such as, but notlimited to: experience_structure_ID, experience_structure_data,OC_code(s), and/or the like.

A experiences table (aka “OC” table) 3619 h may include fields such as,but not limited to: OC_code, parent_OC_code, child_OC_code(s), title(s),job_description(s), educational_requirement(s),experience_requirement(s), salary_range, tasks_work_activities, skills,category, keywords, related occupations, state_ID(s), attribute_ID(s),and/or the like.

An attributes table 3619 i may include fields such as, but not limitedto: attribute_ID, attribute_name, attribute_type, attribute_weight,attribute_keywords, confidence_value, rating_value, comment,comment_thread_ID(s), salary, geographic_location, hours_of_work,vacation_days, benefits, attribute_transition_value,attribute_transition_weight, education_level, degree,years_of_experience, state_ID(s), OC_code(s), user_ID(s), and/or thelike.

A paths table 3619 j may include fields such as, but not limited to:path_ID, state_path_sequence, state_ID(s), attribute_ID(s), user_ID(s),attribute_key_terms, and/or the like.

A templates table 3619 k may include fields such as, but not limited to:template_ID, state_ID, job_ID, employer_ID, attribute_ID, template data,and/or the like.

A job_listing table 3619 l may include fields such as, but not limitedto: job_listing_ID, institution_ID, job_title, job_description,educational_requirements, experience_requirements, salary_range,tasks_work_activities, skills, category, keywords, related occupations,OC_code, state_ID, attribute_ID(s), user_ID(s), UI_ID(s), and/or thelike.

A institution table (aka “employer” table) 3619 m may include fieldssuch as, but not limited to: institution_ID, name, address,contact_info, preferences, status, industry_sector, description,experience_ID(s), template_ID(s), state_ID(s), attributes,attribute_ID(s), and/or the like.

In one embodiment, the APB database may interact with other databasesystems. For example, employing a distributed database system, queriesand data access by search APB component may treat the combination of theAPB database, an integrated data security layer database as a singledatabase entity.

In one embodiment, user programs may contain various user interfaceprimitives, which may serve to update the APB. Also, various accountsmay require custom database tables depending upon the environments andthe types of clients the APB may need to serve. It should be noted thatany unique fields may be designated as a key field throughout. In analternative embodiment, these tables have been decentralized into theirown databases and their respective database controllers (i.e.,individual database controllers for each of the above tables). Employingstandard data processing techniques, one may further distribute thedatabases over several computer systemizations and/or storage devices.Similarly, configurations of the decentralized database controllers maybe varied by consolidating and/or distributing the various databasecomponents 3619 a-m. The APB may be configured to keep track of varioussettings, inputs, and parameters via database controllers.

The APB database may communicate to and/or with other components in acomponent collection, including itself, and/or facilities of the like.Most frequently, the APB database communicates with the APB component,other program components, and/or the like. The database may contain,retain, and provide information regarding other nodes and data.

The APBs

The APB component 3635 is a stored program component that is executed bya CPU. In one embodiment, the APB component incorporates any and/or allcombinations of the aspects of the APB that was discussed in theprevious figures. As such, the APB affects accessing, obtaining and theprovision of information, services, transactions, and/or the like acrossvarious communications networks.

The APB component enables the management of advancement pathstructuring, and/or the like and use of the APB.

The APB component enabling access of information between nodes may bedeveloped by employing standard development tools and languages such as,but not limited to: Apache components, Assembly, ActiveX, binaryexecutables, (ANSI) (Objective-) C (++), C# and/or .NET, databaseadapters, CGI scripts, Java, JavaScript, mapping tools, procedural andobject oriented development tools, PERL, PHP, Python, shell scripts, SQLcommands, web application server extensions, web developmentenvironments and libraries (e.g., Microsoft's ActiveX; Adobe AIR, FLEX &FLASH; AJAX; (D)HTML; Dojo, Java; JavaScript; jQuery(UI); MooTools;Prototype; script.aculo.us; Simple Object Access Protocol (SOAP);SWFObject; Yahoo! User Interface; and/or the like), WebObjects, and/orthe like. In one embodiment, the APB server employs a cryptographicserver to encrypt and decrypt communications. The APB component maycommunicate to and/or with other components in a component collection,including itself, and/or facilities of the like. Most frequently, theAPB component communicates with the APB database, operating systems,other program components, and/or the like. The APB may contain,communicate, generate, obtain, and/or provide program component, system,user, and/or data communications, requests, and/or responses.

The CSEs

The CSE component 3655 is a stored program component that is executed bya CPU. Similarly as discussed regarding APB in 3635, in one embodiment,the CSE component incorporates any and/or all combinations of theaspects of the CSE that was discussed in the previous FIGS. 1-14B. TheCSE component may communicate to and/or with other components in acomponent collection, including itself, and/or facilities of the like.Most frequently, the CSE component communicates with the APB database,particularly the state structure database table, operating systems,other program components, and/or the like. The CSE may contain,communicate, generate, obtain, and/or provide program component, system,user, and/or data communications, requests, and/or responses. It shouldbe noted in one embodiment, the CSE component may have its own databasecomponent, including a state structure table. As such, the CSE affectsaccessing, generation obtaining and the provision of information,services, advancement states, transactions, and/or the like acrossvarious communications networks.

Distributed APBs

The structure and/or operation of any of the APB node controllercomponents may be combined, consolidated, and/or distributed in anynumber of ways to facilitate development and/or deployment. Similarly,the component collection may be combined in any number of ways tofacilitate deployment and/or development. To accomplish this, one mayintegrate the components into a common code base or in a facility thatcan dynamically load the components on demand in an integrated fashion.

The component collection may be consolidated and/or distributed incountless variations through standard data processing and/or developmenttechniques. Multiple instances of any one of the program components inthe program component collection may be instantiated on a single node,and/or across numerous nodes to improve performance throughload-balancing and/or data-processing techniques. Furthermore, singleinstances may also be distributed across multiple controllers and/orstorage devices; e.g., databases. All program component instances andcontrollers working in concert may do so through standard dataprocessing communication techniques.

The configuration of the APB controller will depend on the context ofsystem deployment. Factors such as, but not limited to, the budget,capacity, location, and/or use of the underlying hardware resources mayaffect deployment requirements and configuration. Regardless of if theconfiguration results in more consolidated and/or integrated programcomponents, results in a more distributed series of program components,and/or results in some combination between a consolidated anddistributed configuration, data may be communicated, obtained, and/orprovided. Instances of components consolidated into a common code basefrom the program component collection may communicate, obtain, and/orprovide data. This may be accomplished through intra-application dataprocessing communication techniques such as, but not limited to: datareferencing (e.g., pointers), internal messaging, object instancevariable communication, shared memory space, variable passing, and/orthe like.

If component collection components are discrete, separate, and/orexternal to one another, then communicating, obtaining, and/or providingdata with and/or to other component components may be accomplishedthrough inter-application data processing communication techniques suchas, but not limited to: Application Program Interfaces (API) informationpassage; (distributed) Component Object Model ((D)COM), (Distributed)Object Linking and Embedding ((D)OLE), and/or the like), Common ObjectRequest Broker Architecture (CORBA), local and remote applicationprogram interfaces Jini, Remote Method Invocation (RMI), SOAP, processpipes, shared files, and/or the like. Messages sent between discretecomponent components for inter-application communication or withinmemory spaces of a singular component for intra-applicationcommunication may be facilitated through the creation and parsing of agrammar. A grammar may be developed by using standard development toolssuch as lex, yacc, XML, and/or the like, which allow for grammargeneration and parsing functionality, which in turn may form the basisof communication messages within and between components. For example, agrammar may be arranged to recognize the tokens of an HTTP post command,e.g.:

-   -   w3c-post http:// . . . Value1

where Value1 is discerned as being a parameter because “http://” is partof the grammar syntax, and what follows is considered part of the postvalue. Similarly, with such a grammar, a variable “Value1” may beinserted into an “http://” post command and then sent. The grammarsyntax itself may be presented as structured data that is interpretedand/or other wise used to generate the parsing mechanism (e.g., a syntaxdescription text file as processed by lex, yacc, etc.). Also, once theparsing mechanism is generated and/or instantiated, it itself mayprocess and/or parse structured data such as, but not limited to:character (e.g., tab) delineated text, HTML, structured text streams,XML, and/or the like structured data. In another embodiment,inter-application data processing protocols themselves may haveintegrated and/or readily available parsers (e.g., the SOAP parser) thatmay be employed to parse communications data. Further, the parsinggrammar may be used beyond message parsing, but may also be used toparse: databases, data collections, data stores, structured data, and/orthe like. Again, the desired configuration will depend upon the context,environment, and requirements of system deployment.

The entirety of this application (including the Cover Page, Title,Headings, Field, Background, Summary, Brief Description of the Drawings,Detailed Description, Claims, Abstract, Figures, and otherwise) shows byway of illustration various embodiments in which the claimed inventionsmay be practiced. The advantages and features of the application are ofa representative sample of embodiments only, and are not exhaustiveand/or exclusive. They are presented only to assist in understanding andteach the claimed principles. It should be understood that they are notrepresentative of all claimed inventions. As such, certain aspects ofthe disclosure have not been discussed herein. That alternateembodiments may not have been presented for a specific portion of theinvention or that further undescribed alternate embodiments may beavailable for a portion is not to be considered a disclaimer of thosealternate embodiments. It will be appreciated that many of thoseundescribed embodiments incorporate the same principles of the inventionand others are equivalent. Thus, it is to be understood that otherembodiments may be utilized and functional, logical, organizational,structural and/or topological modifications may be made withoutdeparting from the scope and/or spirit of the disclosure. As such, allexamples and/or embodiments are deemed to be non-limiting throughoutthis disclosure. Also, no inference should be drawn regarding thoseembodiments discussed herein relative to those not discussed hereinother than it is as such for purposes of reducing space and repetition.For instance, it is to be understood that the logical and/or topologicalstructure of any combination of any program components (a componentcollection), other components and/or any present feature sets asdescribed in the figures and/or throughout are not limited to a fixedoperating order and/or arrangement, but rather, any disclosed order isexemplary and all equivalents, regardless of order, are contemplated bythe disclosure. Furthermore, it is to be understood that such featuresare not limited to serial execution, but rather, any number of threads,processes, services, servers, and/or the like that may executeasynchronously, concurrently, in parallel, simultaneously,synchronously, and/or the like are contemplated by the disclosure. Assuch, some of these features may be mutually contradictory, in that theycannot be simultaneously present in a single embodiment. Similarly, somefeatures are applicable to one aspect of the invention, and inapplicableto others. In addition, the disclosure includes other inventions notpresently claimed. Applicant reserves all rights in those presentlyunclaimed inventions including the right to claim such inventions, fileadditional applications, continuations, continuations in part,divisions, and/or the like thereof. As such, it should be understoodthat advantages, embodiments, examples, functional, features, logical,organizational, structural, topological, and/or other aspects of thedisclosure are not to be considered limitations on the disclosure asdefined by the claims or limitations on equivalents to the claims.

What is claimed is:
 1. An objective advancement benchmarkprocessor-implemented method, comprising: obtaining a seeker identifier;identifying a desired current advancement state for the seekeridentifier; obtaining a selection for a characteristic for benchmarkingthe desired current advancement state; querying a vocational graph statestructure with the desired current advancement state to identify otherseekers having experienced the desired current advancement state whereinthe vocational graph state structure is a datastructure comprised of aninterconnected graph topology of state nodes; querying an attributesdatabase with identifiers of the other seekers and the characteristic;aggregating attributes query results from the attributes database andcomputing a base line value for the selected characteristic; querying anattributes database with the selected characteristic for the seeker forseeker characteristic results; comparing the seeker characteristicresults to the aggregated query results; generating a datastructure forvisualization of the comparison of the seeker characteristic results tothe aggregated query results; and providing the generated datastructureto a requester.
 2. The method of claim 1, further, comprising:displaying the generated datastructure for visualization of thecomparison of the seeker characteristic results to the aggregated queryresults to the requester.
 3. The method of claim 2, further, comprising:determining if more than one characteristic is selected; obtainingweights for the more than one characteristic if more than onecharacteristic is selected; and affecting comparison based on theweights.
 4. The method of claim 3, wherein the weights are defaultweights.
 5. The method of claim 3, wherein the weights are provided by aseeker.
 6. An objective advancement benchmark processor-implementedsystem, comprising: means to obtain a seeker identifier; means toidentify a desired current advancement state for the seeker identifier;means to obtain a selection for a characteristic for benchmarking thedesired current advancement state; means to query a vocational graphstate structure with the desired current advancement state to identifyother seekers have experienced the desired current advancement statewherein the vocational graph state structure is a datastructurecomprised of an interconnected graph topology of state nodes; means toquery an attributes database with identifiers of the other seekers andthe characteristic; means to aggregate attributes query results from theattributes database and compute a base line value for the selectedcharacteristic; means to query an attributes database with the selectedcharacteristic for the seeker for seeker characteristic results; meansto compare the seeker characteristic results to the aggregated queryresults; means to generate a datastructure for visualization of thecomparison of the seeker characteristic results to the aggregated queryresults; and means to provide the generated datastructure to arequester.
 7. The system of claim 6, further, comprising: means todisplay the generated datastructure for visualization of the comparisonof the seeker characteristic results to the aggregated query results tothe requester.
 8. The system of claim 7, further, comprising: means todetermine if more than one characteristic is selected; means to obtainweights for the more than one characteristic if more than onecharacteristic is selected; and means to affect comparison based on theweights.
 9. The system of claim 8, wherein the weights are defaultweights.
 10. The system of claim 8, wherein the weights are provided bya seeker.
 11. An objective advancement benchmark processor-readablenon-transitory medium storing a plurality of processing instructions,comprising issuable instructions by a processor to: obtain a seekeridentifier; identify a desired current advancement state for the seekeridentifier; obtain a selection for a characteristic for benchmarking thedesired current advancement state; query a vocational graph statestructure with the desired current advancement state to identify otherseekers have experienced the desired current advancement state whereinthe vocational graph state structure is a datastructure comprised of aninterconnected graph topology of state nodes; query an attributesdatabase with identifiers of the other seekers and the characteristic;aggregate attributes query results from the attributes database andcompute a base line value for the selected characteristic; query anattributes database with the selected characteristic for the seeker forseeker characteristic results; compare the seeker characteristic resultsto the aggregated query results; generate a datastructure forvisualization of the comparison of the seeker characteristic results tothe aggregated query results; and provide the generated datastructure toa requester.
 12. The medium of claim 11, further, comprising: displaythe generated datastructure for visualization of the comparison of theseeker characteristic results to the aggregated query results to therequester.
 13. The medium of claim 12, further, comprising: determine ifmore than one characteristic is selected; obtain weights for the morethan one characteristic if more than one characteristic is selected; andaffect comparison based on the weights.
 14. The medium of claim 13,wherein the weights are default weights.
 15. The medium of claim 13,wherein the weights are provided by a seeker.
 16. An objectiveadvancement benchmark apparatus, comprising: a memory; a processordisposed in communication with said memory, and configured to issue aplurality of processing instructions stored in the memory, wherein theprocessor issues instructions to: obtain a seeker identifier; identify adesired current advancement state for the seeker identifier; obtain aselection for a characteristic for benchmarking the desired currentadvancement state; query a vocational graph state structure with thedesired current advancement state to identify other seekers haveexperienced the desired current advancement state wherein the vocationalgraph state structure is a datastructure comprised of an interconnectedgraph topology of state nodes; query an attributes database withidentifiers of the other seekers and the characteristic; aggregateattributes query results from the attributes database and compute a baseline value for the selected characteristic; query an attributes databasewith the selected characteristic for the seeker for seekercharacteristic results; compare the seeker characteristic results to theaggregated query results; generate a datastructure for visualization ofthe comparison of the seeker characteristic results to the aggregatedquery results; and provide the generated datastructure to a requester.17. The apparatus of claim 16, further, comprising: display thegenerated datastructure for visualization of the comparison of theseeker characteristic results to the aggregated query results to therequester.
 18. The apparatus of claim 17, further, comprising: determineif more than one characteristic is selected; obtain weights for the morethan one characteristic if more than one characteristic is selected; andaffect comparison based on the weights.
 19. The apparatus of claim 18,wherein the weights are default weights.
 20. The apparatus of claim 18,wherein the weights are provided by a seeker.